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STRATEGIC AND OPERATIONAL ISSUES
IN THE INTEGRATED MANAGEMENT OF AN
AIRPORT – AN OPERATIONS
MANAGEMENT APPROACH
Sultan Sulaiman Alodhaibi
BSc, MSc (Mathematical Science)
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy (Research)
School of Chemistry, Physics and Mechanical Engineering
Science and Engineering Faculty
Queensland University of Technology
2019
Strategic and Operational issues in the integrated management of an airport – An operations management
approach i
Keywords
Airport modelling; passenger flow analysis; airport operational planning;
performance evaluation; arrival patterns; simulation; Optimisation; capacity
expansion; Simulated Annealing; Discrete-Event Simulation.
ii Strategic and Operational issues in the integrated management of an airport – An operations management
approach
Abstract
The global air transport industry is expanding rapidly. New approaches to airport
management are required to ensure that ever-increasing consumer demand is met with
adequate developments of ground operational and processing facilities; particularly
those related to effective and safe processing of passenger flows. The solution to this
problem requires the development of a new generation of fast, reliable decision-
making tools to quickly mobilise the human and technical resources available at
modern airports. Operational research aimed at developing novel airport optimisation
simulations to empower efficient management decisions is therefore a rapidly
advancing field. The research conducted highlights the improvements made in our
understanding of passenger flow modelling to date. These models can be classified as
either ‘analytical’, ‘simulation’, or ‘hybrid’ models, giving decision support
capabilities at all levels of detail: from macroscopic, through mesoscopic, to
microscopic. However, despite the current developments in understanding passenger
flow, the literature suggests that an aggregate model, integrating both outbound and
inbound processes, is still needed.
The main aim of this research is to develop a generic, holistic simulation model
that can optimise passenger flow in an international airport. To achieve such a goal,
the holistic model has been split into three phases. The first phase uses Discrete-Event
Simulation (DES) to develop a generic model of passenger flow through the outbound
processes of an international airport. The DES is built using ExtendSim V9.2 simulator
software from Imagine That. Starting from this simulation of outbound processes, the
DES was also used to investigate how the arrival pattern of outbound passengers at the
front door affects international terminal operations. Included in this are major
outbound processes such as check-in, security screening, and immigration.
Experiments demonstrated that different arrival patterns have a significant impact on
the performance of operational processes, so the best policy for passenger arrival time
can be determined. The second phase is developing a simulation model for inbound
processes, while the third phase is integrating the Advanced Resource Management
(ARM) approach inside the simulation model. In this way, an overall view of airport
operations can be realised, helping operations managers identify potential bottlenecks,
Strategic and Operational issues in the integrated management of an airport – An operations management
approach iii
the optimal utilisation of available resources, and both typical and maximal capabilities
with the available staff resources.
The model supports what-if and trade-off analyses by inclusion of a problem-
oriented approach. Hence, best practice can be identified for staff allocation or the
opening and closing of processing points counters. The simulation results demonstrate
that flight schedules have a large impact on passenger flows. The proposed simulation
framework and model can be used to predict ahead of time the effect of different flight
schedules and may be used as a feedback mechanism to improve the simulation model
before implementation. The results also demonstrated that airport operations
performance was significantly affected by different arrival distribution patterns, i.e.
the rate of arrivals at the airport based on the policy of opening check-in counters prior
to scheduled departure time. The developed ARM model balanced the average waiting
time and operation hours since the staff is only allocated if needed.
In summary, studying passenger flow within an international terminal using DES
allowed the integration of outbound and inbound flow processes to enhance
operational efficacy. By integrating the airport simulation model within an analytical
optimisation framework, the model can determine where additional resources should
be best allocated to reduce the overall cost of waiting. The ARM can be a decision
support tool and efficiently used to support and model real-world airport staff
allocation planning problems.
iv Strategic and Operational issues in the integrated management of an airport – An operations management
approach
List of Publications
1. Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. 2017.
"Framework for Airport Outbound Passenger Flow Modelling." Procedia
Engineering 174:1100-9. doi: https://doi.org/10.1016/j.proeng.2017.01.263.
2. Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. “Impact of
Passenger-Arrival Patterns in Outbound Processes of Airports." Procedia
manufacturing Engineering 30:323-30. doi:
https://doi.org/10.1016/j.promfg.2019.02.046.
3. Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. “A model
to simulate passenger flow congestion in airport environment.” International
Journal of Engineering & Technology” (In press).
4. Alodhaibi, Sultan, Robert L. Burdett, and Prasad TK. D. V. Yarlagadda. “A
review of the challenges in airport terminal planning and future directions”.
(Submitted).
5. Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. “A
framework for sharing staff between outbound and inbound airport processes”.
(To be submitted).
6. Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. “An
analytical optimization framework for airport terminal capacity planning”. (To be
submitted).
Strategic and Operational issues in the integrated management of an airport – An operations management
approach v
Table of Contents
Keywords .................................................................................................................................. i
Abstract .................................................................................................................................... ii
List of Publications ................................................................................................................. iv
Table of Contents ......................................................................................................................v
List of Figures ......................................................................................................................... ix
List of Tables ..........................................................................................................................xv
List of Abbreviations ........................................................................................................... xvii
Statement of Original Authorship ....................................................................................... xviii
Acknowledgements ............................................................................................................... xix
Chapter 1: Introduction ...................................................................................... 1
1.1 Background and motivation ............................................................................................2
1.2 Research problem ...........................................................................................................4
1.3 Research objectives ........................................................................................................5
1.4 Research significance and innovation.............................................................................5
1.5 Thesis outline ..................................................................................................................7
Chapter 2: Literature Review ............................................................................. 9
2.1 Overview ........................................................................................................................9
2.2 Current issues in airport planning and management .....................................................10 2.2.1 Passenger flow issues .........................................................................................10 2.2.2 Security issues ....................................................................................................13 2.2.3 Staff allocation issues .........................................................................................14
2.3 Studying complex systems ...........................................................................................17
2.4 Analytical methods .......................................................................................................18
2.5 Simulations methods .....................................................................................................21 2.5.1 Simulation models for passenger flow ...............................................................24 2.5.2 Simulation models of security processes ............................................................30
2.6 Optimisation methods ...................................................................................................32
2.7 Summary of the reviewed literature..............................................................................41
2.8 Knowledge gap identified .............................................................................................42
2.9 Formulation of research scope and research contributions ...........................................42
Chapter 3: Simulation Model Framework for the Outbound Passenger
Processes at an International Airport .................................................................... 43
3.1 Overview ......................................................................................................................43
3.2 The conceptual framework ...........................................................................................44
vi Strategic and Operational issues in the integrated management of an airport – An operations management
approach
3.3 Passenger flow characteristics...................................................................................... 46
3.4 Outbound processes modelling .................................................................................... 47 3.4.1 Arrival at the terminal ........................................................................................ 49 3.4.2 Check-in module ................................................................................................ 51 3.4.3 Security screening module ................................................................................. 54 3.4.4 Immigration module .......................................................................................... 55 3.4.5 Boarding procedure module ............................................................................... 57
3.5 ExtendSim models for outbound processes .................................................................. 57 3.5.1 Hierarchical blocks ............................................................................................ 59 3.5.2 ExtendSim modules description ......................................................................... 59
3.6 Numerical testing ......................................................................................................... 79 3.6.1 Impact on arrival process ................................................................................... 80 3.6.2 The impact on terminal facilities ....................................................................... 81
3.7 Chapter summary ......................................................................................................... 83
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory
Processes
……………………………………………………………………………………85
4.1 Introduction .................................................................................................................. 85
4.2 Development of passenger arrival process model ........................................................ 86
4.3 Case study 1: Impacts of different time before departure values ................................. 88 4.3.1 Behaviour of CDF of time before flight. ........................................................... 89 4.3.2 Behaviours of arrival pattern ............................................................................. 90 4.3.3 Results of simulation and discussion ................................................................. 90
4.4 Case study 2: Impacts of different mean values ........................................................... 94 4.4.1 Behaviour of CDF .............................................................................................. 95 4.4.2 Behaviours of arrival pattern ............................................................................. 96 4.4.3 Results of the simulation and discussion ........................................................... 97
4.5 Selection of best time to arrive at the airport based on the normal distribution ......... 100 4.5.1 Selection of the best scenario at each process ................................................. 102 4.5.2 Aggregation of all processes ............................................................................ 103
4.6 Chapter summary ....................................................................................................... 107
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound
Airport Processes. ................................................................................................... 109
5.1 Introduction ................................................................................................................ 109
5.2 Inbound passenger flows modelling ........................................................................... 110 5.2.1 Outline of inbound flow processes .................................................................. 110 5.2.2 Inbound process simulation modelling ............................................................ 111
5.3 Inbound ExtendSim module description ..................................................................... 113 5.3.1 Block 1: Hierarchy blocks for inbound processes ........................................... 115 5.3.2 Block 2: Creating inbound passengers’ entities of an arrived flight ................ 115 5.3.3 Block 3: Inbound security module ................................................................... 122 5.3.4 Block 4: Duty free............................................................................................ 126 5.3.5 Block 5: Inbound immigration and customs module ....................................... 127 5.3.6 Block 6: Baggage claim module ...................................................................... 132 5.3.7 Block 7: Inbound quarantine module ................................................................ 133
5.4 Integrated inbound and outbound processes .............................................................. 140 5.4.1 Advanced resource management (ARM) ......................................................... 140
Strategic and Operational issues in the integrated management of an airport – An operations management
approach vii
5.4.2 Mechanism of development of algorithms .......................................................142 5.4.3 The logic of development algorithms ...............................................................144 5.4.4 Input data of integrated model ..........................................................................145 5.4.5 Categories of algorithms...................................................................................146
5.5 Chapter summary ........................................................................................................157
Chapter 6: Case Study - Validation of the Simulation Model ..................... 158
6.1 Introduction ................................................................................................................158
6.2 KKAI operational data ................................................................................................161
6.3 Model application and simulation process ..................................................................165
6.4 Simulation results and analysis .....................................................................................168 6.3.1 Description of Terminal 1 results .....................................................................169 6.3.2 Description of Terminal 2 results .....................................................................173 6.3.3 Results analysis and discussion ........................................................................177
6.5 Chapter summary ........................................................................................................180
Chapter 7: Application of Advanced Resource Management (ARM) ........ 182
7.1 Introduction ................................................................................................................182
7.2 Overview of airport resource management .................................................................183 7.2.1 Model demonstration ........................................................................................184 7.2.2 General input data ............................................................................................184
7.3 Simulation results and analyses ..................................................................................185 7.3.1 Static Allocation Base Case method .................................................................186 7.3.2 Dynamic resource allocation method ...............................................................191 7.3.3 Comparison of overall impact of static and dynamic allocation models ..........194 7.3.4 Identify the variation of static and dynamic allocation methods ......................196
7.4 Dynamic approach demonstration ..............................................................................197 7.4.1 Adding and removing staff polices for non-integrated processes ....................197 7.4.2 Sharing staff policy for integrated processes ....................................................200
7.5 Chapter summary ........................................................................................................203
Chapter 8: An Analytical Optimization Framework ................................... 204
8.1 Problem description and formulation .........................................................................205 8.1.1 Model notation .................................................................................................205
8.2 Simulated annealing....................................................................................................206 8.2.1 Simulated annealing algorithm description ......................................................209
8.3 Numerical testing and analysis ...................................................................................211
8.4 Chapter summary ........................................................................................................217
Chapter 9: Conclusion ..................................................................................... 218
9.1 Introduction ................................................................................................................218
9.2 Summary and discussion ............................................................................................218
9.3 Research contributions................................................................................................221 9.3.1 Framework for airport outbound passenger flow modelling ............................222 9.3.2 Investigating the effect of arrival patterns of departing passengers on the
departure terminal operations ...........................................................................222 9.3.3 Advanced resource management strategies ......................................................223
viii Strategic and Operational issues in the integrated management of an airport – An operations management
approach
9.3.4 Development of a novel holistic model for facilitating outbound and
inbound processes ............................................................................................ 224 9.3.5 Strategic and operational planning techniques ................................................ 224 9.3.6 Practice contribution ........................................................................................ 225
9.4 Limitations and future research directions ................................................................. 225
Bibliography ........................................................................................................... 229
Appendices .............................................................................................................. 239
Strategic and Operational issues in the integrated management of an airport – An operations management
approach ix
List of Figures
Figure 2 - 1: The relationships between consecutive and non-consecutive
processes (Hsu et al., 2014). ........................................................................ 20
Figure 2 - 2: Mathematical model of the SSCP using a Jackson open queuing
network (Dorton & Liu, 2015) ..................................................................... 20
Figure 2 - 3: An illustration of the ExtendSim model used for simulation and
optimisation of an airport baggage handling system (Savrasovs, et al.
2009). ........................................................................................................... 25
Figure 2 - 4: Scheme for processing the inbound international passengers using
the Anylogic software package (Curcio, et al. 2007). .................................. 26
Figure 2 - 5: Grid element scheme for the probabilistic discrete determination
of human motion from a given position to the nearby positions on the
grid (Schultz & Fricke, 2011). ..................................................................... 27
Figure 2 - 6: Diagram of stock and flow (Manataki & Zografos, 2009b) .................. 29
Figure 3 - 1: Overview of an airport’s terminal processes, including outbound
and inbound processes ................................................................................. 44
Figure 3 - 2: Airport system model. ........................................................................... 45
Figure 3 - 3: Airport outbound processes (Shuchi, 2016). ......................................... 46
Figure 3 - 4: The input modelling of an outbound simulation model. ....................... 48
Figure 3 - 5: Flowchart for generating outbound passenger attributes. ..................... 49
Figure 3 - 6: The relationship between departing passengers’ arrival times and
the type of flight (Ashford et al. 2011). ....................................................... 50
Figure 3 - 7: Flowchart of check-in processing at international airport. .................... 52
Figure 3 - 8: Module hierarchy of check-in system. .................................................. 53
Figure 3 - 9: Flowchart of screening checkpoints for processing at the
international airport. ..................................................................................... 55
Figure 3 - 10: Flowchart of immigration system at international airport. .................. 56
Figure 3 - 11: Proposed logic design for outbound system consisting of eight
blocks. .......................................................................................................... 60
Figure 3 - 12: Input data represented by passenger attributes. ................................... 60
Figure 3 - 13: Block 1: ExtendSim simulation for outbound system. ........................ 62
Figure 3 - 14: Block 2: Prioritise arrivals. ................................................................. 63
Figure 3 - 15: Block 2: Algorithm for assigning passenger high priority. ................. 64
Figure 3 - 16: Block 3: Assigning check-in type using the decision module. ........... 65
Figure 3 - 17: Block 3: Self-service module. ............................................................. 66
Figure 3 - 18: Block 3: Hierarchical block for check-in group module. .................... 66
Figure 3 - 19: Block 4: Decision module for selecting class of travellers. ................ 67
x Strategic and Operational issues in the integrated management of an airport – An operations management
approach
Figure 3 - 20: Block 4: Decision modules for number of bags and check-in
type. .............................................................................................................. 68
Figure 3 - 21: Block 4: Delay time module. ............................................................... 69
Figure 3 - 22: Block 4: Workstation control module. ................................................ 69
Figure 3 - 23: Block 5: Diplomatic decision module. ................................................ 70
Figure 3 - 24: Block 5: Queue system module. .......................................................... 71
Figure 3 - 25: Block 5: Processing time distribution module. ................................... 71
Figure 3 - 26: Block 5: Security first failure module. ................................................ 72
Figure 3 - 27: Block 5: Workstation control module. ................................................ 72
Figure 3 - 28: Block 6: Probability of random explosive check module. .................. 73
Figure 3 - 29: Block 6: Random check decision module ........................................... 73
Figure 3 - 30: Block 6: Processing time distribution at random explosive check. ..... 74
Figure 3 - 31: Block 7: SmartGate user decision module check. ............................... 75
Figure 3 - 32: Block 7: SmartGate processing time distribution................................ 75
Figure 3 - 33: Block 7: Immigration queue system. ................................................... 76
Figure 3 - 34: Block 7: Immigration workstation control module. ............................ 76
Figure 3 - 35: Block 8: Walking time distribution for boarding gate module. .......... 77
Figure 3 - 36: Block 8: Walking time distribution to boarding gate module. ............ 78
Figure 3 - 37: Block 8: Flowchart for boarding procedure algorithms. ..................... 79
Figure 3 - 38: Arrivals patterns for 100% flights full. ............................................... 80
Figure 3 - 39: Arrivals patterns for 50% flights full. ................................................. 80
Figure 3 - 40: Security queue length 100% flights full. ............................................. 82
Figure 3 - 41: Security queue length 50% flights full. ............................................... 82
Figure 3 - 42: Immigration queue length 100% flights full. ...................................... 82
Figure 3 - 43: Immigration queue length 50% flights full. ........................................ 82
Figure 4 - 1: Flowchart for modelling passenger arrivals at the international
terminal ........................................................................................................ 87
Figure 4 - 2: CDF of passengers arriving before flights for a given mean (µ):
(a) µ = 60 min; (b) µ = 90 min; (c) µ = 120 min; (d) µ = 150 min; (e)
µ = 180 min (f) µ = 210 min. ....................................................................... 89
Figure 4 - 3: Departing passenger arrival profiles at airport terminal for
different (Ω) under given (µ): (a) µ = 60 min; (b) µ = 90 min; (c) µ =
120 min; (d) µ = 150 min; (e) µ = 180 min (f) µ = 210 min. ....................... 91
Figure 4 - 4: Queue lengths of different time before flight given µ = 60 .................. 93
Figure 4 - 5: Queue lengths of different time before flight given µ = 90 .................. 93
Figure 4 - 6: Queue lengths of different time before flight given µ = 120 ................ 94
Strategic and Operational issues in the integrated management of an airport – An operations management
approach xi
Figure 4 - 7: CDF of passengers arriving at airport for flight (i) for a given time
before the flight under different (µ): time of passenger arriving (a) Ω =
120 min; (b) Ω = 150 min; (c) Ω = 180 min; (d) Ω = 210 min; (e) Ω =
240 min ........................................................................................................ 95
Figure 4 - 8: Departing passenger arrival profiles at airport terminal for
different (µ) given (Ω): (a) Ω = 120 min; (b) Ω = 150 min; (c) Ω =
180 min; (d) Ω = 210 min; (e) Ω = 240 min ................................................ 96
Figure 4 - 9: Queue lengths of different mean value at a given time before
flight ............................................................................................................. 99
Figure 4 - 10: Check-in queue length and waiting time for all scenarios ................ 102
Figure 4 - 11: Security screening queue length and waiting time for all
scenarios ..................................................................................................... 102
Figure 4 - 12: Immigration queue length and waiting time for all scenarios ........... 103
Figure 4 - 13: (a-g) the impacts of different arrival patterns based on the
priority for each processes ......................................................................... 106
Figure 5 - 1: An illustration of inbound passenger facilitation processes (Wu et
al., 2014). ................................................................................................... 110
Figure 5 - 2: Flowchart of the upper level of the inbound process flow model ....... 112
Figure 5 - 3: The input modelling of the inbound simulation model ....................... 113
Figure 5 - 4: Block 1: The high level of inbound flow modelling ........................... 115
Figure 5 - 5: Block 2: Structure of the hierarchical block ‘passengers
disembarking’ ............................................................................................ 116
Figure 5 - 6: Inbound flight attributes ...................................................................... 116
Figure 5 - 7: Algorithm for creating inbound passenger attributes .......................... 117
Figure 5 - 8: Inbound passenger attributes ............................................................... 118
Figure 5 - 9: Block 2: Mechanism of linking inbound passenger attributes with
the ExtendSim model.................................................................................. 119
Figure 5 - 10: Block 2: Walking speed module ....................................................... 120
Figure 5 - 11: Block 2: Arrival calculating gate distance module ........................... 121
Figure 5 - 12: Block 3: Walking time module ......................................................... 121
Figure 5 - 13: Block 3: The hierarchy module of x-ray and routing for random
check .......................................................................................................... 122
Figure 5 - 14: Block 3: Simulated queue of x-ray check and hierarchy block of
workstation ................................................................................................. 123
Figure 5 - 15: Block 3: Characteristics of processing items .................................... 124
Figure 5 - 16: Block 3: Storing the outputs of security ............................................ 124
Figure 5 - 17: Block 3: Random explosive decision module ................................... 125
Figure 5 - 18: Block 3: queuing and processing time characteristics of random
explosive check .......................................................................................... 126
xii Strategic and Operational issues in the integrated management of an airport – An operations management
approach
Figure 5 - 19: Block 4: Assign duty free attributes module ..................................... 127
Figure 5 - 20: Flow chart of inbound immigration checkpoint process ................... 128
Figure 5 - 21: Block 5: Logic design of inbound immigration checkpoint
process ........................................................................................................ 129
Figure 5 - 22: Block 5: Inbound SmartGate user decision module .......................... 129
Figure 5 - 23: Block 5: Inbound immigration queue module ................................... 130
Figure 5 - 24: Block 5: Process characteristics of inbound immigration ................. 131
Figure 5 - 25: Block 5: Logic design of the SmartGate module .............................. 131
Figure 5 - 26: Block 6: Baggage claim decision queue module............................... 132
Figure 5 - 27: Block 6 baggage claim delay time module ....................................... 133
Figure 5 - 28: Flow chart for quarantine process ..................................................... 134
Figure 5 - 29: Block 6: Logic chart of quarantine module ....................................... 135
Figure 5 - 30: Block 7: Inbound declaration decision module ................................. 136
Figure 5 - 31: Block 7: Declaration queue module .................................................. 137
Figure 5 - 32: Block 7: Inbound immigration queue module ................................... 138
Figure 5 - 33: Block 7: Inbound Quarantine queue module ..................................... 138
Figure 5 - 34: Block 7: Nothing to declare queue module ....................................... 139
Figure 5 - 35: Block 7: Quarantine workstation for nothing to declare line ............ 140
Figure 5 - 36: Flowchart framework for ARM model ............................................. 142
Figure 5 - 37: Resource allocation dialog for global array ...................................... 143
Figure 5 - 38: Dynamic link between parameter tables and ExtendSim ................... 144
Figure 5 - 39: Staff attributes for the ARM model ................................................... 145
Figure 5 - 40: Flowchart algorithm for airline staff allocation module ................... 147
Figure 5 - 41: Flowchart for integrated module for boarding procedure ................. 148
Figure 5 - 42: Flowchart algorithm of quarantine staff management module ......... 150
Figure 5 - 43: Flowchart 1-2 of security resource allocation management .............. 152
Figure 5 - 44: Flowchart 2-2 of security resource allocation management .............. 153
Figure 5 - 45: flowchart algorithm of immigration resource allocation ................... 155
Figure 6 - 1: The terminals and runways of the King Khalid international
airport. ...................................................................................................................... 159
Figure 6 - 2: Passenger movement numbers at KKIA from 2005-2016 (Statista,
2019) .......................................................................................................... 159
Figure 6 - 3: Scheme of passenger flow types at KKIA terminals (Kloosterziel
et al., 2009). ................................................................................................ 160
Figure 6 - 4: Processing time distributions for departure processes of Terminal
1 of KKIA .................................................................................................. 163
Strategic and Operational issues in the integrated management of an airport – An operations management
approach xiii
Figure 6 - 5: Processing time distributions for departure processes of Terminal
2 of KKIA .................................................................................................. 164
Figure 6 - 6: Flowchart of KKIA departure flow processes (researcher’s
illustration) ................................................................................................. 166
Figure 6 - 7: Process of calculating cycle time ........................................................ 168
Figure 6 - 8: Arrival pattern and profile of Terminal 1 passengers. ........................ 170
Figure 6 - 9: a, b Terminal 1 check-in process results; c, d Terminal 1 security
screening process results; e, f Terminal 1 immigration process results ..... 172
Figure 6 - 10: Arrival pattern of Terminal 2 passengers and entering Terminal 2
distribution. ................................................................................................ 173
Figure 6 - 11: a, b Terminal 2 check-in process results; c, d Terminal 2 security
screening results; e, f Terminal 2 immigration process results .................. 175
Figure 7- 1: Example of ExtendSim database for output data .................................. 186
Figure 7- 2: Check-in average waiting time using the SABC method ..................... 187
Figure 7- 3: Influence of the SABC method on boarding procedures ..................... 188
Figure 7- 4: Security screening average waiting time using the SABC method ...... 189
Figure 7- 5: Immigration average waiting time using the SABC method ............... 190
Figure 7- 6: Quarantine average waiting time using the SABC method.................. 190
Figure 7- 7: Check-in average waiting time using the dynamic resource
allocation method ....................................................................................... 191
Figure 7- 8: Influence of dynamic resource allocation method on boarding
procedures .................................................................................................. 192
Figure 7- 9: Security screening average waiting time using the dynamic
resource allocation method ........................................................................ 193
Figure 7- 10: Immigration average waiting time using the dynamic resource
allocation method ....................................................................................... 193
Figure 7- 11: Quarantine average waiting time using the dynamic resource
allocation method ....................................................................................... 194
Figure 7- 12: Static allocation method results.......................................................... 195
Figure 7- 13: Dynamic resource allocation method results ..................................... 195
Figure 7- 14: Variation in the SABC method .......................................................... 196
Figure 7- 15: Variation in the dynamic resource allocation approach ..................... 196
Figure 7- 16: Check-in facility results of adding and removing staffing policies ... 199
Figure 7- 17: Quarantine facility results of adding and removing staffing
policies ....................................................................................................... 200
Figure 7- 18: Outcomes of simulated immigration staff sharing rules ................... 201
Figure 7- 19: Security screening facility results of sharing staffing policies ........... 203
Figure 8- 1: General steps of the simulated annealing ............................................. 207
Figure 8- 2: Selecting the best initial parameters ..................................................... 208
xiv Strategic and Operational issues in the integrated management of an airport – An operations management
approach
Figure 8- 3: snapshot of simulated annealing results ............................................... 211
Figure 8- 4: SA optimisation results using the random method of creating new
solutions ..................................................................................................... 215
Figure 8- 5: AS optimisation results using the method of creating new solution
using local technique .................................................................................. 217
Strategic and Operational issues in the integrated management of an airport – An operations management
approach xv
List of Tables
Table 2 - 1: Summary of the recent issues associated with airport operations ......... 16
Table 2 - 2: Comparison between the three types of simulation (Ma, 2013;
Owen, 2013) ................................................................................................. 22
Table 2 - 3: Summary of models used to address airport problems ........................... 38
Table 3 - 1: Summaries of major elements and processing facilities of check-in
module.......................................................................................................... 54
Table 4 -1: Selection of Ω values under a fixed µ values .......................................... 88
Table 4-2: Detailed output of ExtendSim simulation model for case study 1 ............ 92
Table 4-3: Selection of Ω values under different µ values ........................................ 95
Table 4-4: Detailed output of ExtendSim simulation model ...................................... 98
Table 4 - 5: Summary of the simulation results…………………………………....101
Table 4- 6: Illustration of different conditions to select the best scenario………....104
Table 4-7: Summary of the results of selection of the best policy of time before
flight………………………………………………………………………………..107
Table 6 - 1: Summary of model default parameters at the KKIA international
airport. ........................................................................................................ 169
Table 6 - 2: Comparisons of waiting time in queue and cycle time at check-in,
security and immigration between the simulation data and real time
data of Terminal 1. ..................................................................................... 178
Table 6 - 3: Comparisons of waiting time in queue and cycle time at check-in,
security and immigration between the simulation data and the real
time data of Terminal 2. ............................................................................. 178
Table 6 - 4: Comparisons of waiting time in queue and cycle time at check-in,
security and immigration between the simulation data and the real
time data at Brisbane International Airport. .............................................. 179
Table 7 - 1: Summary of common operational input data for the experiments ....... 185
Table 7 - 2: Summary of eligible sharing polices .................................................... 202
Table 8- 1: Summary of the input data .................................................................... 212
Table 8- 2: summary of simulated annealing results using random search
technique .................................................................................................... 213
Table 8- 3: summary of simulated annealing results using local search
technique .................................................................................................... 214
Table 8- 4: Check-in additional resource results using the random technique ........ 214
Table 8- 5: Check-in additional resource results using local technique.................. 216
xvi Strategic and Operational issues in the integrated management of an airport – An operations management
approach
Table 9- 1: Comparisons of developed ARM results with Kierzkowski and
Kisiel (2016) .............................................................................................. 224
Strategic and Operational issues in the integrated management of an airport – An operations management
approach xvii
List of Abbreviations
ABS Agent based simulation
ARM Advanced Resource Management
BHS Baggage Handling Systems
CPM Capacity Planning Model
DES Discrete Event Simulation
FIDS Flight Information Display System
GPSS General Purpose Simulation System
IATA International Air Transport Association
ICAO International Civil Aviation Organization
KPIs Key Performance Indicators
LOS Level of Service
SA Simulated Annealing
SD System Dynamic
SSCP Security Screening Checkpoint’s
PAX Passengers
VBA Visual Basic for Applications
xviii Strategic and Operational issues in the integrated management of an airport – An operations
management approach
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the best
of my knowledge and belief, the thesis contains no material previously published or
written by another person except where due reference is made.
Signature:
Date: 17/07/2019
QUT Verified Signature
Strategic and Operational issues in the integrated management of an airport – An operations management
approach xix
Acknowledgements
I would like to express my sincere gratitude to my Principle Supervisor Professor
Prasad K.D.V. Yarlagadda, and my Associate Supervisor Dr. Robert Burdett for their
continuous support and guidance throughout this thesis. This work would not have
been possible without their encouragement, insightful comments and valuable advice.
Also, I would like to thank Professor Clinton Fookes from Airport of the future project
for his kind support by provide me with resource and information that I need to conduct
my research.
I extend my thanks to AbdulAziz Abu Abat a director of event & logistics
support at Saudi Telecom for his kind support for helping me to contact with Abdulaziz
Al-Ruwais a Project manager at Riyadh International airport for sharing the
information that I need.
I would like to thank the staff at the Science and Engineering Faculty and HDR
Support Officers who provide me with an excellent research environment and patiently
answered my questions regarding my enrolment and research progress.
Special thanks to my mother Norah Alshamkh and to my father Sulaiman
Alodhaibi who the reason of being here and for their great love and advices. My sincere
thanks go to my wife Bushra and my lovely daughter Norah for their support during
tough and difficult time to complete this PhD journey.
Finally, my thanks go to my friends for all their love and encouragement. Words
cannot express how grateful I am to all of you.
Chapter 1: Introduction 1
Chapter 1: Introduction
In 2017, the number of airline travellers exceeded 4 billion globally, an increase
in global air transport demand of about 8.1% from 2016 (ICAO, 2018), a number that
will continue to increase and is predicted to exceed 7.1 billion by 2035 (International
Air Transport Association (IATA). Airport management and airlines have discussed
the possibility of changing and updating several policies related to flight schedules,
staff allocation and other operational policies to accommodate future demand growth,
and to provide better quality of services and security. An international airport terminal
is a large and complex system, since it involves inbound and outbound passenger flow
processes, each with unique operations. Some airports have a slightly different process
and new airports designed in the future may require further changes to the standard
process in light of new security concerns being faced in our modern world. Safety
concerns in recent times have caused many changes to security screening procedures
which affect passenger throughput times. After September 11, 2001, when terrorists
brought down the twin towers in New York using passenger planes, airport security
has become more critical. Another problem facing modern airports is the limited
infrastructure and staffing capacity, such as numbers of common check-in counters
and numbers of personnel available, to deal with increasing passenger numbers.
Due to the complexity of the airport terminal, there is a need for new effective
management approaches to ensure that the skyrocketing demands in air travel are met
with adequate developments of ground operational and processing facilities. In this
thesis a holistic simulation framework is developed using Discrete-Event Simulation
(DES) to simulate entire passenger flow processes within international terminals. DES
is used because it can handle stochastic system and temporal variation demands (Chiu,
2002; Guizzi, Murino, & Romano, 2009; Rauch & Kljajić, 2006). The motivation and
background for this thesis is presented in section 1.1. The following section 1.2
describes the research problem and research question. Section 1.3 presents the aims
and objectives of this research, while the contribution statement and the methodology
used to address research gaps are presented in section 1.4. Section 1.5 completes the
chapter with an outline of the thesis structure.
2 Chapter 1: Introduction
1.1 BACKGROUND AND MOTIVATION
In recent years, airports have played a significant role in economic growth,
connecting cities and countries around the world. Numerous passengers choose to
travel by airline in preference to other transportation modes such as trains, buses and
private cars. Based on the International Civil Aviation Organization (ICAO, 2018)
report for 2017, the number of airline travellers globally exceeded 4 billion, a growth
in global air transport demand of about 8.1% compared with the previous year
(Statista, 2018). The direct, indirect and induced contributions of airline travel to the
global GDP in 2017 was US$776 trillion, including almost 2.78 million jobs generated
globally (IATA, 2017b). In Australia, based on the report by the Bureau of
Infrastructure Transport and Regional Economics (2012), the number of air travellers
increased annually at the rate of about 5%, from 27 million in 1977–1978 to 135.1
million in 2010–2011. The same report indicated that the number of passenger
movements in Australia would reach approximately 279.2 million in 2030–2031
(BITRE, 2012).
One of the major causes for this substantial and rapid increase in air transport
demand is the rapid growth of the international trade and globalisation of industries.
Another significant cause lies with rapidly increasing tourism – over 54% of
international tourists now travel by air (IATA, 2017a). This increase in air transport
demand imposes significant strains on air travel operations and facilities that are
expected to keep up with the growing passenger flows. This includes airport capacity
and ability to process the increasing numbers of passengers with high efficiency and
minimum delay. The required expansion of airport capacity may be limited by the
available resources (e.g. limited available land), environmental impacts and lengthy
approval processes (Barnhart, Fearing, Odoni, & Vaze, 2012). In addition, extension
of major airport infrastructure is typically time-consuming and costly, which
highlights the need for the development of smart systems and methods to improve
airport performance within the available infrastructure limitations.
It is conventional to sub-divide or classify airport operations into those relevant
to the arrival procedures of incoming passengers and departure procedures for
outgoing passengers. The arrival processes and facilities include disembarking,
immigration, baggage claim, and quarantine procedures. The departure processes and
facilities include check-in, security screening, immigration and customs, boarding and
Chapter 1: Introduction 3
take-off procedures. It is these departure flow processes that have the greatest impact
on the entire operation of passenger terminals and other elements of the airport.
According to De Neufville, Odoni, Belobaba, and Reynolds (2013), the departure
process requires significantly more time than the arrival process because it sometimes
involves services provided to transit passengers. Consequently, most research focuses
on achieving greater efficiency in the departure process to alleviate congestion in
airport terminals (Du, Yu, Jiang, & Ji, 2015; Guizzi et al., 2009; Manataki & Zografos,
2009b; Odoni & de Neufville, 1992; Solak, Clarke, & Johnson, 2009; Wu &
Mengersen, 2013; Zidarova & Zografos, 2011).
Airports are very complex, interlinked systems and any operational problems
with any of their elements can jeopardise the performance of other elements, creating
significant bottlenecks, long passenger queues, congestion and overall delays (De
Neufville & Odoni, 2003; Manataki & Zografos, 2009b; Zografos & Madas, 2006).
For example, disruption, congestion and uneven passenger inflow into the terminal
processing points, caused by the operation of the landside element (including the
infrastructure and facilities associated with the arrival of passengers to the airport),
could have a significant impact on the performance of the terminal (such as passenger
boarding and take-off procedures). Similarly, the airside element can influence the
performance of landside elements, for example, through an excessive flow of inbound
or transit passengers, which could take staff from the landside elements.
Different parts of the airport have been analysed or optimised in isolation, for
example, analysis and simulations conducted for outbound flow processes within
airport terminals to achieve optimal and most efficient passenger processing (Du et al.,
2015; Guizzi et al., 2009; Manataki & Zografos, 2009b; Solak et al., 2009; Wu &
Mengersen, 2013; Zidarova & Zografos, 2011). Some simulations and modelling have
examined the departure processes located in the landside including passenger arrival
at the airport and facilities external to the terminal (Correia & Wirasinghe, 2013; Wu
& Mengersen, 2013; Zhou, Huang, Jia, & Jiang, 2014), while others have examined
passenger boarding and plane take-off procedures that are located in the airside area
(Bazargan, 2007; Budesca & Juan, 2014; Jacquillat & Odoni, 2015; Van Landeghem
& Beuselinck, 2002). There has, however, been little holistic research on the
performance and capacity issues for the whole airport system including both outbound
and inbound types of passenger flow processes. The lack of a comprehensive
4 Chapter 1: Introduction
overarching model can lead to incorrect assessments and the adoption of incorrect
policies. This can result in significant financial losses due to unexpected delays and
inefficient use of the airport resources and related infrastructure. For example, the costs
of airport delays in the US alone exceeded US$32.9 billion in 2007 and were around
$US41 billion in 2008 (Ferguson, Kara, Hoffman, & Sherry, 2013). In addition,
operational efficiency at an airport can also directly impact safety, security and
customer satisfaction (Rauch & Kljajić, 2006), which can also be associated with
significant direct and indirect financial and other losses.
1.2 RESEARCH PROBLEM
As discussed above, the demand for air travel is growing, but the decision support tools
are not fully optimal. The complexity of airport terminal systems creates the need for
models that can provide an integrated view for all terminal operations. As most
existing tools and models are focused only on individual processes and address
fragmented sections of decision making procedures in airports (Wu & Mengersen,
2013; Zografos, Madas, & Salouras, 2013), a holistic simulation model integrated with
advanced resource management and analytical frameworks developed in this thesis.
The proposed modelling includes simulation and analysis of the impacts of
passenger arrival patterns associated with different arrival modes corresponding to
different means of passenger transportation to the airport facilities. In addition, the
proposed research involves modelling and simulating the impacts of the flow of the
inbound passengers on the processing of outbound passengers. A holistic view is
required to not only study the performance of outbound and inbound flow processes,
but to also investigate the possible interactions between the two elements with respect
to resource allocation policies. In this thesis the following research questions are
addressed:
Question 1: How can a holistic simulation model be utilised/developed to
analyse/optimise the passenger flows in an international airport by
integrating inbound and outbound processes?
Question 2: Can the simulation model identify the impacts of different
passenger arrival patterns and help identify the best passenger arrival
policy?
Chapter 1: Introduction 5
Question 3: Can advanced resource management (i.e. staff allocation,
opening and closing of check-in counters, etc.) policies be integrated within
a holistic simulation model?
Question 4: Can a simulation model be used within an analytical
optimization framework to improve the efficiency and operation of airports?
1.3 RESEARCH OBJECTIVES
The general aim of this research is to develop comprehensive approaches to
model and optimise airport operations, which would involve the integrated
consideration and analysis of the two airport terminal elements of a modern airport –
outbound and inbound. The main objectives of this research can be summarised as
follows:
Develop a simulation framework simulation model using DES for outbound
passengers
Investigate the impacts of different passenger arrival patterns and determine
the best policy for outbound passengers.
Develop advanced resource management strategies and analyse the effects
on airport operations. Use results to provide direction and best policies for
more effective staff allocation and reallocation within the international
airport terminal.
Integrate inbound flow processes with outbound processes to investigate the
effect of inbound passenger flows on outbound flows and vice versa.
Develop an analytical optimisation framework to be used within the
simulation model for capacity planning.
1.4 RESEARCH SIGNIFICANCE AND INNOVATION
This thesis makes several contributions to the understanding of passenger flows
within airport terminals. It contributes to the development of a new decision-making
support tool aimed at operational improvements and performance optimisation of the
airport terminals, considering key performance indicators such as average/maximum
waiting time and average/maximum queue length. Using this tool, airport operators
can (i) effectively identify any potential bottlenecks, (ii) optimally utilise available
6 Chapter 1: Introduction
resources, and (iii) determine the typical and maximum capabilities and capacity flows
utilising the available personnel and other resources. This will lead to increased
passenger satisfaction, balanced operating hours and acceptable waiting times in front
of processing stations (Andreatta, De Giovanni, & Monaci, 2014; Dowling,
Krishnamoorthy, Mackenzie, & Sier, 1997; Kirk, 2013b; Wu & Mengersen, 2013;
Yamada et al., 2017; Zografos et al., 2013).
The outcomes of the research will not be limited to Australian airports and will
also significantly contribute to the development of the general knowledge of efficient
operational management practices in the air travel industry. The methodology
developed in this research will facilitate further development of airport modelling and
simulations. The innovation of this thesis is based on the following achievements
filling in the current existing knowledge gaps:
The development of a new methodology based on a unique combination of
an optimisation approach within simulation using ExtendSim simulation
software. This methodology can capture the stochastic nature (the effect of
uncertainty) of the airport system and its dynamic changes over time
(Sachidananda, Erkoyuncu, Steenstra, & Michalska, 2016; Yamada et al.,
2017). To achieve this goal, the methodology is divided into four phases:
o Development of framework for outbound processes flow to predict the
effect of different flight schedules. The framework may be used as a
feedback mechanism for improvement before implementation. It is also
used to identify the effect of different arrival patterns on the
performance of check-in, security and immigration processes.
o Integration of outbound and inbound process flows to understand
terminal process capabilities and facilitate the efficient processing of
increasing numbers of air travel passengers with minimum delay. The
structure of the model is based on a hierarchical model using ExtendSim
hierarchy block. This structure gives the model more flexibility to
enable rapid and easy modification according to airport design.
o Development of the advanced resource management approaches to
dynamically allocate and reallocate personnel, and to identify effective
opening and closing counters policies.
Chapter 1: Introduction 7
o Determination of where additional resources should be allocated using
a capacity expansion model to reduce passenger time spent waiting in
front in front of terminal processes and improve airport operational
efficiency.
The developed model is a flexible decision support tool to deal with
passenger demands and other unexpected phenomena that might occur in
the terminal. This is done by conducting what-if scenarios to evaluate
alternatives and possible changes in policies related to passenger patterns,
dynamic opening and closing of counters, allocating and reallocating of
staff. The proposed approach is significant as it can be used by people who
are non-simulation experts.
This project makes a new contribution to the general area of operational research
through combining the effects and synergistic impacts of the two elements of airport
terminals—outbound and inbound—which to date have largely been considered as
separate entities.
1.5 THESIS OUTLINE
This thesis consists of eight chapters, including this introductory chapter which
presents the research problem, motivation and significance, aims and objectives and
the thesis outline. The rest of the thesis has been organised as follows:
Chapter 2 presents the literature review relating to passenger flows modelling,
security issues modelling and staff allocation issues modelling. The literature is
grouped into three major models including analytical, simulation and hybrid models.
In Chapter 3, a generic framework for outbound passenger flow modelling is
introduced with respect to the input modelling for the ExtendSim model. In Chapter 4
the model is used to investigate the impacts of different arrival patterns on the
performance of departure operational processes. The outputs of the simulation model
are demonstrated to be more intuitive than those of other models. The model can help
airport operators better understand the distribution of arrivals to the airport to provide
better levels of services.
Chapter 5 extends the simulation model including inbound processes flow then
integrated with the advanced resource management approach to manage allocation and
8 Chapter 1: Introduction
reallocation of staff within airport terminals. This approach can provide insights into
the causes of bottlenecks, passenger logistics, and the relationships between different
processes that share a set of commonalities and functions.
Chapter 6 presents the validation processes of the simulation model to
demonstrate that this model accurately represents an actual airport terminal using King
Khalid International Airport (KKIA) as a case study.
Chapter 7 discusses the application of Advanced Resource Management (ARM)
modelling and how the model can be used to study the variation in the complex
environment of allocating and reallocating staff as well as resource sharing policies.
Chapter 8 presents an analytical optimization framework to perform capacity
planning in order to improve the efficiency of the airport and to meet future demand.
Finally, Chapter 9 presents and discusses the conclusions of the research including the
study limitations and recommendations for further research.
Chapter 2: Literature Review 9
Chapter 2: Literature Review
2.1 OVERVIEW
This chapter reviews past research focused on the current problems associated
with airports’ operational performance and the challenges of planning. It also provides
insight into the capabilities and limitations of the existing models. The models consider
the analysis and performance optimisation of separate airport elements. For example,
analyses and simulations of airport terminals have been conducted to achieve optimal
passenger processing (Du et al., 2015; Guizzi et al., 2009; Manataki & Zografos,
2009b; Solak et al., 2009; Wu & Mengersen, 2013; Zidarova & Zografos, 2011).
Simulations and models were also designed for landside elements, including passenger
arrival at the airport via the landside infrastructure and facilities external to the
terminal (Correia & Wirasinghe, 2013; Eilon & Mathewson, 1971; Tošić, 1992; Wu
& Mengersen, 2013; Zhou et al., 2014), and for airside elements, including passenger
boarding and plane take-off procedures (Bazargan, 2007; Budesca & Juan, 2014;
Jacquillat & Odoni, 2015; Van Landeghem & Beuselinck, 2002).
A wide range set of parameters are used to characterise passenger flow,
including flight schedules, service rates and resources, and the facilitation process and
associated passenger characteristics (e.g. nationality, as it influences which Customs
lane the passenger can use). Section 2.2 explains the current issues associated with
airport planning and management including passenger flow problems and other related
issues such as security and staff allocation problems. Section 2.3 provides an overview
of the types of approaches, such as analytic, simulation and hybrid approaches, used
to solve the proposed issues. Section 2.4 and section 2.5 discuss analytical and
simulation models for the purpose of airport passenger flow and resource allocation
issues. Section 2.6 describes the use of hybrid and optimisation models for a better
understanding of the characteristics of the airport terminal system and its dynamics.
10 Chapter 2: Literature Review
2.2 CURRENT ISSUES IN AIRPORT PLANNING AND MANAGEMENT
2.2.1 Passenger flow issues
Passenger flow is a result of numerous individuals’ movement within airport
terminals. The primary activities of passengers moving in such an environment are
travelling and walking between processes and facilities to be served. This section
analyses existing research conducted to address the issues of passenger flows within
airports. The irregular flows occurring in airport terminal areas represent a significant
management challenge, for instance, determining the number of service counters to
open, and personnel allocation and reallocation issues (Fonseca, Casanovas, & Ferran,
2014; Wu & Mengersen, 2013). Different policies are associated with departure and
arrival patterns, for example, employee rostering and operation schedules, and with
respect to particular airport operations and activities, such as check-in, duty free
shopping, access control, and waiting areas (Fonseca et al., 2014; Manataki &
Zografos, 2009b; Zografos & Madas, 2006). Passenger flow in airport terminals can
be divided into three main categories: departing passengers, arriving passengers, and
transferring passengers (Ma, 2013; Manataki & Zografos, 2009b).
Another significant problem when studying passenger flow is capturing
stochastic elements. This is because, as Guizzi et al. (2009) argued, passengers behave
differently inside airports according to their previous experiences. Thus, in order to
assist decision makers at the airport terminal to address sudden and unforeseen
congestion conditions, extensive research has been conducted on uncertainty. Yamada
et al. (2017) examined links between passenger behaviour and facilities and identified
numerous kinds of probable congestion circumstances. Ma et al. (2011, 2012, 2013)
investigated the uncertainty factors that can impact the route choice decision making
of passengers, as well as the complex behaviours outside the required processes. To
overcome this complexity, researchers integrated the discretionary activities of
passengers with the standard processing units within the terminal. These discretionary
activities were in alignment with major factors such as walking distance and remaining
time. Passenger flow in new terminals was demonstrated using simulation models of
the passenger flow in the new terminal of Heathrow Airport (Beck, 2011). This model
sought to understand the system before the terminal opened. In addition, some
researchers have included group dynamics in passenger flow models (Cheng, 2014;
Cheng, Reddy, Fookes, & Yarlagadda, 2014). These researchers argued that group
Chapter 2: Literature Review 11
dynamics could have a significant impact on the performance and utilisation of
terminal facilities. The effect of group dynamics can be seen in the dwell time at each
processing unit, the level of service (LOS) at the processing units, and in the
discretionary activities (Cheng, 2014).
Estimating departure passenger flow is a prerequisite to improving airport
terminal resources for meeting dynamic travel demands. For this reason, Sun and
Schonfeld (2015) proposed a simulation-based approach to cater to the issue. Firstly,
an estimate of the number of passengers for every booked flight is done. Then, the
passenger behaviour of every passenger is simulated through randomly generating
departure time dependent on probabilistic distribution. This is followed by a count of
the number of passengers for every time interval. The efficiency or suggested approach
was validated by collecting data from Beijing Capital International Airport.
Research looking at limited resources in airports, such as terminal check-in staff
and security equipment, has used prediction methods to determine how best to use
these resources, and how this can change the level of service in the terminal. Mu,
Cheng, Zhang, and Zhang (2014) designed a method based on neural networks and
support vector regression to predict passenger traffic in the departure terminal of
Harbin Taiping International Airport. Similar work was conducted by Chiang and
Taaffe (2014). Their study used a simulation model as a theoretical model to estimate
occupancy of the zone in the concourse. The methodology involved a finger-pier
concourse with twelve gates and four groups of moving walkways to predict passenger
occupancy in every zone. The model proposed in the research can be used for effective
decision-making to manage concourse operations under any Level of Service. The
flexibility in simulation tools developed through research provides airport researchers
and planners with significant information to make informed decisions when
considering passenger conveyance systems and corridor congestion.
Passenger flow analysis and performance evaluation have been widely reported
in the literature, for instance, Rauch and Kljajić (2006) developed a DES to evaluate
the performance of existing departure processing facilities by focusing on ticketing,
check-in, immigration, and boarding in order to improve passenger satisfaction. By
selecting proper thresholds, the length of the queue is controlled in a specific range.
Nikoue, Marzuoli, Clarke, Feron, and Peters (2015) generalised the idea of passenger
walk time to a model which is independent of origin gate, by deploying mixed models.
12 Chapter 2: Literature Review
They focused on simulations and system dynamics to model airport performance. The
study used following information sources: Flight Information Display System (FIDS)
dataset; DIMIA datasets (stamps at immigration); and Dwell time for all the
simulation results compared to actual wait times, it was observed that simulation times
were much higher, nearly 2 pm each day, than actual results.
Past research has also considered the dynamic operation of terminal systems (e.g.
Manataki & Zografos, 2009, 2010; Nikoue et al., (2015). For example, Manataki and
Zografos (2009b, 2010) designed their model to answer questions regarding
operational concepts, including the number of staff needed for a particular process,
and the percentage of passengers who use e-check-in or online check-in. In Manataki
and Zografos (2009b), two parameters (walking speed and path distance distributions)
were used to determine the flow of passengers from one area to another.
Adacher, Flamini, Guaita, and Romano (2017) pointed out that the quick rise in
passenger traffic and reduced expansion of airport capacity have limited the capability
of airports to maintain satisfactory customer services. Hence, the authors propsoed an
optimization model based on a Surrogate method which provided a specific layout and
passenger flow, decided the number of security control checkpoints and check-in desks
to reduce cost function. Tests were performed using the Napoli-Capodichino (IT)
airport as a case study. As it was a preliminary study, the solution approach was applied
to one airline firm. The results demonstrated that queues at check-in desks satisfy the
tolerance threshold Nt. On the other hand, there is an additional discomfort cost for
security control check-in points. The study, in the end, provides an efficient algorithm
to determine various critical resources for the airport terminal departure operations. It
is recommended that it is implemented with a group of firms and compared with other
optimization algorithms. This will help in optimizing the departure area of existing
airports and designing the size of the departure area for a new airport.
The capacity-planning problem has been studied with respect to transient
demand patterns. An example of this is the study carried out by Solak et al. (2009) to
determine the optimal design for airport terminals, and to expand the capacity for
different processing areas in the presence of uncertainty. Sun and Schonfeld (2015)
also investigated the uncertainty within the terminal. This included the non-linearity
of facility performance functions as represented by delay level as a function of
utilisation rates of capacity, and demand fluctuations as indicated by uncertainties in
Chapter 2: Literature Review 13
traffic predictions. It is believed that passenger departure flow is an important process
for any airport facility because of the fixed departure time of flights.
This section has identified current problems associated with passenger flow such
as congestion in queueing areas, delays at workstations, rate of passenger arrival at the
airport, group dynamics. The next section examines security issues in airports.
2.2.2 Security issues
Safety concerns in recent times have caused many changes to security screening
procedures and this impacts passenger throughput times. After the incident on
September 11 2001, when terrorists brought down the World Trade Centre, New York
City, USA using passenger planes, airport security has dramatically increased.
Therefore, security managers at airports require methods for quantifying changes in
the level of security to avoid terrorist attacks (Skorupski & Uchroński, 2018).
There have been a number of publications looking at airport security. These
publications discuss the problem of security control, particularly with regards to
forbidden objects being carried into the restricted areas of airports (van Boekhold,
Faghri, & Li, 2014). The existing research can be categorised based on its related topic
areas, including the significance of the security screening system in airport operations,
the capacity of security screening areas, and dynamic system management (Dorton &
Liu, 2015; Kierzkowski & Kisiel, 2016, 2017; Leone & Liu, 2011; Skorupski &
Uchroński, 2016; van Boekhold et al., 2014).
Security screening systems are influenced by many factors, such as the
distribution of arrivals, limited physical resources (security line), and operational
factors. Dorton and Liu (2015) investigated the main external factors influencing the
security screening checkpoint’s (SSCP) operational efficiency. This study took into
account the system’s dependent measures of throughput and cycle time. Furthermore,
Chitty, Yang, and Gongora (2017) stated that airports face pressures of reducing costs
of waiting time at the security lane zone by decreasing the working hours of the lane
and maintaining passengers’ service level. In this regard, evolutionary methods can
reduce both objectives.
Kierzkowski and Kisiel (2015) investigated the impact of the behavioural
characteristics of the operators and passengers on the reliability of the system.
Additionally, they discovered irregular flow of travellers in the security system, which
14 Chapter 2: Literature Review
caused numerous peaks. To solve this problem, the number of staff and technical
resources needs to be increased to ensure the smooth performance of the process. Li,
Gao, Xu, and Zhou (2018) conducted a recent study on numerous passenger strategies
and built mathematical models to portray them; using structures of network queuing
for airport security check-points and conducting various numerical experiments to
compare the performance of numerous queuing structures.
Many recent papers have focused on the problems of allocating the appropriate
number of resources, including to the security zone, and this issue will be discussed in
more detail in the following section.
2.2.3 Staff allocation issues
Currently, airport passengers and operators are faced with issues of providing
fast access to the facilities of the airport and preventing congestion during peak
periods. Delays are caused by ground operations and the efficiency of terminal
processes are assumed to have huge importance. To resolve these issues, many efforts
have been made to improve passenger travel experience and airport management’s
performance. Scheduling can be defined as the allocation of activities or actions to
resources according to specific performance criteria (Spyropoulos, 2000). Some
research models study staff scheduling and staff rostering problems. For example, Blaž
Rodič (2010) proposed the idea of workforce shift allocation and rapid rescheduling,
as per dynamic flight scheduling. (Abdoul Soukour, Devendeville, Lucet, & Moukrim,
2013) discussed staff scheduling issues in security operations. The memetic algorithm
is used to solve the problem, assuming that it is possible to close or open counters
depending on demand using a dynamic programming approach.
Dowling et al. (1997) discussed the development of a software product utilised
to formulate rosters for nearly 500 staff of a main international airline at one of the
busiest airports in the world. The key issue was to establish a robust algorithm that
offers optimised monthly rosters for airport service employees. The study was
successful in describing an overall system and the algorithm to solve rostering issues
linked to the system. Soukour, Devendeville, Lucet, and Moukrim (2012) proposed an
approach to resolve realistic scheduling issues in the airport security service domain.
The authors divided the problem into three steps: days off schedule, shift scheduling,
and staff assignment. Then, they emphasise the last step by offering two algorithms,
global assignment and greedy algorithms, to offer an initial solution. This solution is
Chapter 2: Literature Review 15
improved through Iterative algorithm Destruction/Construction, IDC shifts. All
algorithms are applied in Java and tested on Intel Xeon Quad Core at 2.4 Ghz. IDC
shifts help include further new limitations therefore, it is concluded as the best
solution. Sabar, Montreuil, and Frayret (2012) aimed to use a multi-agent based
algorithm model for personnel scheduling and rescheduling in a dynamic setting of a
fast-paced multi-product assembly centre.
Effective allocation of Ground Service Equipment (GSE)to aircraft standing on
the apron is determined with the help of a framework (Integrated Airport Apron Safety
Fleet Management – AAS) as emerged in the European sponsored project (Andreatta
et al., 2014). The basic model used in research is the conceptual Integer Programming
formulation of Ground-Service Resource Allocation Problem (GRAP). Andreatta et
al. (2014) proposed a fast heuristic approach to display how the issue can be
decomposed into sub-issues. It was recommended that GRAP can be improved to
consider robustness against unforeseen delays which often happen in airport aprons.
Parlar and Sharafali (2008) conducted their research to determine the optimal
number of open counters over a specified period. Lin, Xin, and Huang (2015)
described the problems with ground crew rostering and shift allocation in an attempt
to better manage the opening and closing of check-in counters. Recent research by
Rodič and Baggia (2017) attempted to solve the problem of a lack of schedules for
airport check-in employees, especially regarding work groups and overlapping skills.
A summary of the issues associated with airport operations reported in the
literature is presented in Table 2-1.
16 Chapter 2: Literature Review
Table 2 - 1: Summary of the recent issues associated with airport operations
Airport Area Issues Inbound/Outbound Papers
Terminal entry/boarding Operational efficiency
problems
Customer satisfaction
Outbound Rauch and
Kljajić (2006)
Queuing areas Quality of service to
PAX
Congestion problems
Outbound
Check-in and security Complex traffic in side
departing system
Delays at processing
station
Outbound Guizzi et al.
(2009)
Processing, holding, and
flow facilities of the
airport terminal
Congestion
System complexity
Airport operation
problem
Outbound Manataki and
Zografos (2009)
Discretionary activities
and processing units Complex movement
flows
Uncertainty factors
Outbound Ma, et al. (2011,
2012, 2013)
Airport waiting room Presence of bottlenecks
Waiting room capacity
Level of service
Outbound Ju, Wang, and
Che (2007)
Discretionary activities
and processing units Passenger group
dynamics
Group behaviour
Evacuation strategy
Outbound Cheng (2014)
Check-in/security Passenger traffic flow
Randomness of
passenger flow
Outbound Mu et al., (2014)
Terminal processing
points and passageways Stochastic future
demand
Capacity expansion
Passenger terminal
design
Outbound Solak, Clarke,
and Johnson
(2009)
Terminal
airfield system
cargo facility
Congestion effects
Air traffic growth
Out/inbound Sun and
Schonfeld
(2015)
Security screening Performance of SSCP
Baggage volume of
PAX
Alarm rate of baggage
screening device
Outbound Dorton and Liu
(2015)
Security screening Efficacy characteristics
of carry process
Intensity of
passenger flow
Outbound Kierzkowski
and Kisiel
(2015, 2016)
Chapter 2: Literature Review 17
Factors affecting
reliability
Security screening Acceptable quality of
passenger service
Lack of management
Outbound Kierzkowski
and Kisiel
(2015, 2016)
Check-in concourse
Manpower planning
New terminal
Outbound Beck (2011)
Security screening Workforce demand
days-off scheduling
Shift scheduling
Staff assignment
Outbound Abdoul Soukour
et al. (2013)
Check-in Allocation of resources
for check-in counters
Outbound Parlar and
Sharafali (2008)
Check-in Allocating staff for
check-in
Rostering of ground
flow
Outbound Lin et al. (2015)
Departure procedures Scheduling staff
Ground crew processes
Outbound Rodič and
Baggia (2017)
Departure procedures and
baggage handling system Delay at processing
stations
Process delay
Delay control
Outbound Hsu and Chao
(2014)
Entire departure
processes Human behaviour
Analysed walking
behaviour
Outbound Schultz and
Fricke (2011)
Check-in and security Human resource
allocation
Outbound Siadat, Arain,
and Ruwanpura
(2012)
Terminal gates Passenger experience
Gate scheduling
Gate assignment
problem
Outbound /Inbound Kim, Feron,
Clarke,
Marzuoli, and
Delahaye
(2013)
2.3 STUDYING COMPLEX SYSTEMS
A system has various definitions; most of the proposed definitions are very
similar. The earliest definition was provided by Schmidt and Taylor (1970), who noted
that the system is about of a group of entities, e.g. people, that interact together for
achievement of some logical end. Backlund (2000) defined the system as a set of
elements with interacting parts forming a complex. A similar definition is used by
Miller (1978, p. 17) “A system is a set of interacting units with relationships among
18 Chapter 2: Literature Review
them”. In practice, Law and Kelton (1991, p. 3) says the system is always based on the
objectives of a specific study. This means that the group of entities forming a system
for one study might be only a subgroup of the overall group of another system.
This section discusses different types of models used to solve the current
problems occurring in airport terminal systems. These models predominantly consider
departure systems to measure the performance of workstations and to understand
significant factors that affect system’s performance. Wu and Mengersen (2013)
pointed out that existing airport models can be categorised into four groups, capacity
planning, operational planning and design, security policy and planning, and airport
performance review. These models can be analytic, simulation, and hybrid approaches
as well. They require different levels of detail (e.g. macroscopic, microscopic, and
mesoscopic) and have deterministic and stochastic characteristics (Wu & Mengersen,
2013; Zografos & Madas, 2006). The models capture different performance metrics
for ‘operational efficiency’, including service time, queue length, and throughput. To
bridge this gap, several models have been developed. These methods can be separated
into four categories: analytical, simulation, optimisation, and integrated models (L.
Cheng, Yarlagadda, Fookes, & Yarlagadda, 2014; Law & Kelton, 1991; Tošić, 1992;
Wu & Mengersen, 2013; Zidarova & Zografos, 2011).
2.4 ANALYTICAL METHODS
Most of the earlier literature looking at airport terminals has focused on
analytical models. These models are exemplified by deterministic queue models that
assess significant performance metrics, such as waiting and service time for
passengers, and queue length at individual processing stations (Barbo, 1967; Tošić,
1992; Wu & Mengersen, 2013). A review by Wu and Mengersen (2013) stated that
deterministic models use a graphic form of the cumulative arrival and departure
profiles from the service facility under inspection. The deterministic model’s
advantages are that the two performance measures (queue length and average wait
time) can be easily determined depending on the one-to-one vertical and horizontal
distance between arrival and departure for a given facility (Wu & Mengersen, 2013).
The disadvantage, however, is that these types of models do not take into account
uncertainties in the arrival and departure profiles, and are unable to find the maximum
wait time for a single traveller (Wu & Mengersen, 2013).
Chapter 2: Literature Review 19
To raise throughput of the system of security checks and maintaining safety
standards, a novel hybrid parallel queuing system dependent on passenger
classification is suggested by Hu and Chen (2017). It is a combination of virtual queue
system, modified model M/M/n, and basic queuing model M/M/1. The assumptions in
the study are made regarding security check progress. To address issues and
bottlenecks in the basic model, a properly optimised and modified model M/M/n is
introduced. The results reveal that when n is increased, wait time of M/M/n reduces
quickly which supports the analysis that throughput of M/M/n is much better than
n*M/M/1.
Hsu et al. (2014) developed a queuing model for passengers and their baggage
with different connecting airport terminal facilities. This study divided the departure
process stations into two types. The first type served single flights, including boarding,
loading, and transit flights. The second type served multiple flights of different
airlines, and included immigration, security, and baggage sorting. The authors
proposed an analytical model to investigate the impact of delay propagation among
these processes at Taoyuan International Airport. The relationships between both
sequential and non-sequential processes were investigated. Consecutive processes
refer to the airline services for multiple flights, such as check-in, while non-
consecutive processes refer to the airport services for multiple flights, such as
immigration, security screening, and baggage sorting. The results suggested that the
non-consecutive processes performed better as they provided more buffer time
between different operations and fewer delays from previous facilities (see Figure 2-
1). Hsu et al. (2014) further explored the control strategies for both flight and departure
process delays. In their study, the characteristics of the departure process at airport
terminals were presented by the “time network” of the departure process in order to
identify the required time for completing the process.
20 Chapter 2: Literature Review
Figure 2 - 1: The relationships between consecutive and non-consecutive processes (Hsu et al., 2014).
Dorton and Liu (2015) developed a similar analytical model representing a
queuing network based on a combination of M/G/1, M/G/2 and M/M/1 servers as seen
in Figure 2-2. This study focused on two different factors. The first type was the
internal factors, such as staffing and equipment, and the second type was the external
factors, such as baggage volume and the alarm rate of security screenings.
Figure 2 - 2: Mathematical model of the SSCP using a Jackson open queuing network (Dorton & Liu,
2015)
Chapter 2: Literature Review 21
Solak et al. (2009) divided the airport terminal system into two main areas:
passageways and processing stations (i.e. check-in and security checkpoints). They
proposed an analytic approximation of the maximum commuter delay in the two areas
of the terminal, including walking time, processing time, and queue length. Their
assumptions included the relationship between width of passageway and flow rates,
the measurement of walking speeds, and the fact that processing time changes over a
day. Further expansion of this theory was made by Sun and Schonfeld (2015), who
presented a strategic capacity planning model for airport systems that considered the
uncertainties in forecasting traffic levels. The key issue for conducting such research
is the facility performance functions “delay levels as functions of capacity utilizations
rate” Sun and Schonfeld (2015, p. 1). This function is nonlinear, which complicates
the solution’s design. To solve this problem, the authors proposed a deterministic total
cost minimisation model as a first phase, and then extended it into a stochastic model
by considering the uncertainties in traffic predictions. An outer-approximation
algorithm was used to solve the mixed integer nonlinear problem.
2.5 SIMULATIONS METHODS
Simulation refers to a group of theories, applications and methods that replicate
a real system of behaviour for evaluation and experimentation. Simulation models are
used to mimic complex systems over time by applying assumptions related to a
system’s operations (Diefenbach, 2010). Simulation is a powerful and flexible tool that
can assist the representation of the assumptions of the model and its conditions in
logical and mathematical relationships (Winston & Goldberg, 2004).
Simulation approaches can be classified based on three characteristics. Firstly,
simulations can be either static or dynamic in nature. Static models are not time
sensitive; events have the same validity if they occur a second apart or a year apart.
Dynamic simulations, which are more common, involve events that are time sensitive,
such as a manufacturing process. Secondly, models can be defined as continuous or
discrete. Continuous simulations represent systems that comprise continuous change,
such as pressure levels or fluid levels, whereas discrete event simulation model
systems comprise events that occur at a specific point in time. Discrete simulations are
effective for modelling parts or people that arrive at specific times and undergo
processes at specific times. The operations of an airport terminal are discrete and
would be accurately modelled by DES. Finally, simulations can be deterministic or
22 Chapter 2: Literature Review
stochastic. Deterministic simulations have no random input, meaning that events
always happen at exactly the same time, for instance fixed appointments. Stochastic
simulations are simulations where at least some of the events occur at random times
(Dorton, 2011).
The simulation approaches can be classified into three sets (Table 2-2):
1. Discrete Event Simulation (DSE) modelling
2. System Dynamic (DS) modelling
3. Agent based simulation (ABS) modelling
Table 2 - 2: Comparison between the three types of simulation (Ma, 2013; Owen, 2013)
Discrete Event
Simulation
System Dynamic Agent based
simulation
Demonstration Demonstrate the system
as queues activities,
processes, schedules
Demonstrate the system
as flows and stocks
Proactive and
autonomous agents that
interact with each other
to achieve their aims
The key of problem Randomness related to
interrelated events and
processes
Problem can be
understood by analysing
the causal reaction effects
Individual agent modules
with directions of their
interfaces.
Mathematics Depending on statistical
distributions.
Depending Mathematical
modelling
Based on Algorithms,
simple probability and
logic
Communication ease True illustration of
system
Showing the model
design and numerical
results perfectly
Very good in illustrating
the behaviour of
individual entities.
Model accuracy Because of heavy
dependence on data, the
model processes accurate
valid static model
The accuracy of the
model is high because of
its heavy reliance on data
Difficult to constructs
but they are accurate
models
Chapter 2: Literature Review 23
The overall approach of DES is to deal with randomness related with
interconnected events that leads to system behaviour. The theory of DES has been
applied to numerous fields to obtain insight into the complexity of the system being
studied. It has also been used to analyse the performance of systems (Dorton, 2011).
According to Wolverine Software Company’s website, DES software “allows you to
place your system under a microscope and explore its operation under laboratory
conditions” (Wolverine Software Company, 2014). Banks (2010, p. 12) also points out
that DES “is the modelling of systems in which the state variable changes only at a
discrete set of points in time.” According to the Winter Simulation Conference (WSC),
a well-known and distinguished conference, there are many applications for DES and
it is seen in many disciplines. Examples of these applications are: healthcare services,
supply chain management, transportation modes and traffic, and military applications.
Other good examples of DES systems include banks, warehouses, and gas stations
(Tarshizi, 2014).
DES has many advantages in real-world systems. First, a DES can describe a
real system’s features and characteristics and allows the developer to make any
changes to the study system. It also enables the testing of variations of the system
without affecting the actual system (Diefenbach, 2010). In addition, a DES can assist
with the definition of individual components of a system and the interaction of
components that actually have an impact on the system (Heizer, 1996). Another
significant benefit of a DES is that it provides a cost-effective decision-making tool,
because it permits the minimisation of risks by developers throughout, as they can
discover the correct decision before they make the wrong one. On the other hand, DES
does have some drawbacks. It can be expensive and lengthy in terms of development
and running. A DES also needs a large amount of computational time. According to
Lapin (1994), to overcome the warmup period and allow for a steady state, the model
should be run for sufficient periods.
There is extensive literature on the simulation models used for both airport
terminal modelling and performance measurement analysis. Simulation models have
often been developed to deal with operational problems, although these models
required a more detailed description and more input data from the particular system
under study (Manataki & Zografos, 2009b; Wu & Mengersen, 2013). Additionally,
simulation models require more time and operation than macroscopic models
24 Chapter 2: Literature Review
(Manataki & Zografos, 2009a). Manataki and Zografos (2009b) suggested that most
of the existing simulation models are either too specific in one processing point or are
general simulation platforms for the integration of more than one processing unit. The
latter is required for a sufficient airport model.
2.5.1 Simulation models for passenger flow
A substantial number of simulation models have focused on the evaluation of
passenger flow. One such example of simulation modelling was presented by Eilon
and Mathewson (1973), who used an agent-based simulation model to measure
terminal facility congestion and passenger processing time. The model used
comprehensive sets of parameters, such as flight schedules, service rates, and resources
to describe passenger flow. It also contained facilitation processes and some passenger
characteristics, such as nationality. Similarly, Takakuwa and Oyama (2003) developed
a microscopic simulation model of an airport terminal in order to assess passenger
flow, with a primary focus on international departures. Their model considered the
influence of variables such as flight schedules, passenger nationality, walking speed,
volumes of bags, and passenger group size to analyse the facilitation process capacity.
The results indicated that the number of passengers missing flights could be decreased
if additional staff were added, and if the first and business class check-in counters were
used to process economy and group-class passengers.
Important efforts were made to use software packages, such as Anylogic and
ExtenSim, for modelling some elements of airport operations. For example, Savrasovs,
Medvedev, and Sincova (2009) analysed the performance of Baggage Handling
Systems (BHS) using a simulation method based on discrete event simulation
including optimisation of resources and determination of waiting times (Figure 2-3).
Curcio, Longo, Mirabelli, and Pappoff (2007) demonstrated the potential capabilities
of the Anylogic software package for simulation of the operations for processing
inbound international passengers including immigration services and other processing
elements/services (Figure 2-4). The limitation of this Anylogic model is its neglect of
the interference of the inbound and outbound passengers, for example, through the
competition for passport control service staff and facilities. Nevertheless, both studies
illustrate significant potential of both ExtendSim and Anylogic for efficiently
simulating airport and terminal operations and processes for inbound and outbound
passengers.
Chapter 2: Literature Review 25
Figure 2 - 3: An illustration of the ExtendSim model used for simulation and optimisation of an airport baggage handling system (Savrasovs, et al. 2009).
26 Chapter 2: Literature Review
In addition, Ma et al. (2011) provided a similar microscopic simulation model
represented by an agent-based model that mainly focused on human factors (i.e.
passenger characteristics). The proposed model was used to study the check-in
operations of passengers and their use of discretionary facilities. Similar research was
presented by Beck (2011), who designed a simulation model for the passenger flow in
a new airport terminal of Heathrow International Airport, both before and after its
opening. This paper discussed a number of factors that had to be considered for the
model, such as the characteristics of the passengers.
Figure 2 - 4: Scheme for processing the inbound international passengers using the Anylogic software
package (Curcio, et al. 2007).
Cheng (2014) developed a model using the simulation method for passenger
flow as it had become an important approach in designing and managing airports. Most
researchers have failed to take into account group dynamics when developing
pedestrian flow models. Therefore, for more realistic passenger flow conditions, this
study included group dynamics. An agent-based model was proposed due to its
feasibility and effectiveness as an approach for investigating the movement of
passengers in airports. Similarly, Fonseca et al. (2014) used a microscopic agent-based
model for the Barcelona International Airport to simulate the flow of passengers,
companions, employees, and vehicles. Specification and Description Language (SDL)
Chapter 2: Literature Review 27
was used for the formal representation of the model. This system was used to assess
the initial airport design, and the dynamic optimisation of the terminal management
and operations. Yamada et al. (2017) modelled Japan’s Fukuoka airport’s international
terminal as a Complex Adaptive System. They constructed passenger flow simulations
dependent on the DES model. The authors concluded that it is possible to attain
simulation input data through discussions with stakeholders deploying simulation.
Hence, it is believed that it is possible to lower model uncertainty by continuously
discussing, predicting, and modelling with the stakeholders.
Another interesting approach was based on the simulation of passenger flow in
the airport environment on the basis of grid-based probabilities for non-deterministic
human motion (Schultz & Fricke, 2011). In this model, human motion was
probabilistically determined from one square element of the grid to another based on
the relative probabilities of the corresponding motions (see Figure 2-5). This figure
also shows the separate modelled paths of different individuals superimposed onto a
photograph of the actual place in the airport (Schultz & Fricke, 2011).
Figure 2 - 5: Grid element scheme for the probabilistic discrete determination of human motion from a
given position to the nearby positions on the grid (Schultz & Fricke, 2011).
Rauch and Kljajić (2006) stated that passenger flow could be defined as a
discrete stochastic process. Therefore, discrete event simulation (DES) is often used
to model such a complex system of constraints from a limited infrastructure capacity,
e.g. the airport terminal (Verbraeck & Valentin, 2002). There is extensive literature on
DES being used to analyse departing passenger flow (Guizzi et al., 2009; Novrisal,
Wahyuni, Hamani, Elmhamedi, & Soemardi, 2013; Rauch & Kljajić, 2006). Guizzi et
al. (2009) developed a simulation model aimed at predicting delays using logical and
28 Chapter 2: Literature Review
rational management in the check-in and security checkpoint areas. They took into
account the available capacity, the volume of passengers based on time of day, and
passenger behaviour. The Rockwell Arena simulation software tool was used in this
study to determine the average queue length and waiting time. In contrast, Rauch and
Kljajić (2006) constructed their model using the General Purpose Simulation System
(GPSS), a simulation programming language. The authors analysed departure
passenger flow, from check-in through to boarding, until just before departure, in order
to identify system bottlenecks and capacity. Key factors such as passenger arrival
patterns, passenger service time, flight schedules, and operating processes were
measured. Similarly, (Novrisal et al., 2013) developed their model to analyse
congestion problems in the departure process at Soekarno-Hatta International Airport
in Indonesia. The model’s objectives were to reduce processing and waiting time in
the system. It was discovered that the number of check-in counters needed to be
increased as they had reached maximum utilisation. Maximum utilisation was reached
at approximately 61% of the total time passengers spent in the queues across the
departure process before boarding (Novrisal et al., 2013).
Manataki and Zografos (2009b) proposed a system dynamics modelling
approach as a mesoscopic model that focused on aggregate characteristics while
working at an intermediate level. Their model’s structure was built based on stock and
flow diagrams (see Figure 2-6).
Chapter 2: Literature Review 29
Figure 2 - 6: Diagram of stock and flow (Manataki & Zografos, 2009b)
30 Chapter 2: Literature Review
The stock was used to model the passenger facilitation process and physical
facilities. Stock refers to the state of the system, and flow refers to the rate of change
of the stock. The developed model evaluated the significant factors that affected the
flow elements. These factors were walking time, processing rate, and the number of
service counters.
2.5.2 Simulation models of security processes
Simulation models have been developed to analyse the security screening
process. In 2014, van Boekhold et al. developed their general microscopic simulation
model using ExtendSim software to assess the impact of security screening
performance, as well as the impact of pre-screening. This study aimed to express the
acceptable wait time thresholds for these processes, and to offer suggestions to
mitigate wait times by adjusting various aspects, such as approach, procedures, and
equipment. The main effectiveness measures considered in this study were average
wait time, average service time, average queue length, and average throughput rate. A
similar study was undertaken by Chitty et al. (2017), who studied flexibility when
developing initial schedules for easing the evolutionary dynamic process of re-
optimisation. The research concluded that evolutionary dynamic re-optimisation can
shorten passenger waiting times. The research used various methods to measure
flexibility, such as MaxLaneCoverage, Unopenable, and AverageShiftLength,
alongside decreasing opening hours of security lanes and waiting times for passengers.
Results demonstrated that passenger waiting times were shortened using this approach
for dynamically and static re-optimised schedules.
Dorton and Liu (2015) investigated the SSCP by applying a DES. The
independent variables of the proposed model included baggage volume carried by each
passenger, and the number of suspect bags that needed manual inspection. A simulator
for construction (SimFC) was applied to model the processes of traveller check-in and
security checkpoints at the Calgary International Airport. A case study was presented
using two different scenarios to examine and analyse flights to USA destinations. The
paper revealed a number of points, the most notable of which was that the passenger
wait times were strongly influenced by the available security (Siadat et al., 2012).
Simulation models have been used to study passenger experiences in the area of
security. Research undertaken by Kim et al. (2013) used several simulations of gate
assignments to study airport gate scheduling in order to improve passenger experience.
Chapter 2: Literature Review 31
The data came from previous studies and a major USA airport hub. The first goal of
the study was to minimise the transit time of travellers in passenger terminals. Transit
time included the time spent between the security checkpoint and the gate, between
the gate and the baggage claim area, and between two gates. The results confirmed
that models could improve traffic flow efficiency in passenger terminals on ramps and
help develop the robustness of gate operations.
Dynamic system management has been considered in combination with a
simulation model in the area of security (Kierzkowski & Kisiel, 2015, 2016).
Kierzkowski and Kisiel (2015) developed a simulation model of airport security
screening counters to estimate the impact of the behavioural characteristics of the
operators and passengers on the reliability of the system. The model’s inputs were the
capacity of the area of systems, which included the entry area, the manual area, and
the loading area, as well as actual time of simulation and unloading time (A.
Kierzkowski & Kisiel, 2015). In their second work, Kierzkowski and Kisiel (2016)
developed a simulation model using FlexSim that aimed to improve the efficiency of
security control with respect to the level of safety. In addition, the authors developed
dynamic management algorithms for the security control system operations schedule
in an integrated approach with a simulation model. The results obtained from the
algorithms showed more improvement compared to static management. In static
management, the average time of an air traveller’s stay in the system was 9.538 min
and required 285 work hours. While in dynamic management, the average time of an
air traveller’s stay in the system was about 7 min and required nearly 162 work hours.
The input data of the proposed model includes:
Security control structures, such as counter characteristics and security
control process distribution.
Flight schedules.
The procedure of the security control process (e.g. the consequences of
triggering the walk through metal detector gate, etc.) (Artur Kierzkowski &
Kisiel, 2016).
Kierzkowski and Kisiel (2017), extended their work and applied fuzzy logic
theory for multi-criteria evaluation to study the efficiency, capacity, and level of
service of the security control system. The study focused on three elements:
32 Chapter 2: Literature Review
Capacity of a security control counter.
Efficiency of prohibited items detection.
The passengers’ evaluation of the system.
A very recent and similar study was conducted by Skorupski and Uchroński
(2018), who presented a fuzzy inference system to evaluate overall efficiency of
prohibited items detection during baggage and passenger security screening. The
FASAS (Fuzzy Airport Security Assessment System) tool assists airport management
in security control. This model was created and deployed in a simulation experiment
to display the efficiency of a method to manage a system of security screening at the
airport (Katowice International Airport). The results revealed the performance of the
screening system can be enhanced by upgrading screening devices and by improving
training session frequency. Overall, the results demonstrate that screening
performance can be substantially improved, however, as the required performance
level improves there is a trade-off with personnel training costs and system throughput.
2.6 OPTIMISATION METHODS
To enable better decision and operational planning, significant optimisation
approaches and a mix of two models have been developed to deal with recent airport
issues (Abdoul Soukour et al., 2013; Bertsimas, Lulli, & Odoni, 2011; Dorton & Liu,
2015; Kalasky, Coffman, De Grano, & Field, 2010; Lin et al., 2015). These approaches
have also been developed to deal with the issue of process optimisation, with the aim
being minimal use of technical resources and minimum waiting time in queues (Artur
& Tomasz, 2017; Bevilacqua & Ciarapica, 2010; Roanes-Lozano, Laita, & Roanes-
Macıas, 2004; Solak et al., 2009).
Research looking at the combination of two or more approaches was undertaken
by Ju, Wang, and Che (2007), who presented a combination of simulation and
optimisation methods. A simulation model was developed to understand the current
bottlenecks in related terminal operations and their causes. It was also used to evaluate
the key performance measures of the processing units. The authors used the
optimisation technique to reassign different resources. Du et al. (2015) presented a
distribution optimisation model to decrease departure delays and alleviate congestion
in transit stations caused by the unsteadiness of the distribution of air travellers in
transit modes. The main purpose of the model was to minimise the average departure
Chapter 2: Literature Review 33
time. A genetic algorithm was used to solve this problem. Solak et al. (2009)
considered terminal operations to be a network system and used a multistage stochastic
integer linear programing model to determine the optimal capacity, taking into account
optimal future expansion and desired LOS. The main objective was to minimise the
maximum delay of each passageway and processing station by considering the
variation in demand as a significant constraint.
Regarding the security issue, Dorton and Liu (2015) described a mix of two
models, including a queuing network and a DES for the security screening system.
These models aimed to analyse the main external factors that influenced SSCP
operation efficiency with respect to both system dependent measures, i.e. throughput
and cycle time. SSCP throughput can be defined as the number of passengers that have
arrived in the system and exited from the system during a one hour interval, while the
cycle time is defined as the amount of time spent in the system. Dorton and Liu’s
independent variables included baggage volume carried by each passenger, and the
number of suspect bags that needed manual inspection.
The issue of staff scheduling is widely researched in operational research. An
early example of research on staff scheduling was carried out by Mason, Ryan, and
Panton (1998), they provided information about optimisation and simulation-based
systems for staff rostering of customs staff at the Auckland International Airport. The
development of an integrated approach deploying simulation, integer programming,
and heuristic descent methods is established to identify new-optimal levels of staffing.
The staffing needs are deployed as input to an integer programming model that
distributes part-time and full-time personnel to every period of the working day. The
authors used a simulation model as the basis of their research. Numerous methods,
involving several heuristic procedures, are described for developing personnel
schedules. The results concluded that these techniques lowered staffing levels, created
good quality rosters and ensured passenger processing targets were met. These
established optimisation and modelling tools are now utilised on a daily basis to
produce new rosters. In the same vein, Andreatta et al. (2014) developed heuristics
algorithms to allocate personnel to each ground handling process of an airport. Their
study divided resources into different sets assigned to the ground handling processes
of a particular flight.
34 Chapter 2: Literature Review
Check-in areas have attracted a lot of attention during the development of
optimisation and hybrid models. For example, Hsu, Chao, and Shih (2012) explored
the dynamic distribution of check-in facilities and dynamic allocation of passengers to
reduce total wait time and better use of facilities. The developed model was
implemented at the Taoyuan International Airport. The results demonstrated that
dynamic distribution of check-in facilities can lower waiting times and improve
service counter use rates. These benefits can be improved through dynamic
passengers’ assignment. Two criteria, service counter utilisation rate and waiting time,
were adopted as indicators for required adjustments for distribution of facilities. The
literature on the problem of check-in facilities also concerns approaches to attaining
more effective check-in procedures. The theories previously applied were queuing
theory, dynamic and integer planning, experimental designs, and system simulation.
Moreover, use of the developed assignment model and dynamic allocation model for
air cargo services, as well as other sectors, to attain higher effectiveness and better use
of facilities, requires further research. Similarly, Xin, Lin, Huang, Cheng, and Chong
Teo (2014) used linear programming to determine the optimal number of check-in
counters over a specified period. They justified their approach by demonstrating
improved effectiveness in human resource utilisation, which resulted in fewer counters
and fewer working hours.
Parlar, Rodrigues, and Sharafali (2013) extend the research further by
developing a stochastic model to compare the impacts of static and dynamic policies.
Their model aims to lower total operating cost of counters while satisfying the needs
of airlines and airport authorities. The approach is simple and easy to implement. The
provided model can effortlessly accommodate realistically sized issues with numerous
passengers. This was justified by comparing the performance of dynamic and static
policies using numerical experiments. The study found that static policy must be
selected over the dynamic policy if the number of passengers is lower than 50 and the
static policy cost is lower than aM +V1(0,0) or if passenger number is above 50. The
static policy makes it effortless to find an optimal counter number when the number
of booked passengers is in the hundreds.
Based on the literature, a very common optimization problem in the apron area
is gate assignment which aims to minimise passenger walking distances from both
check-in to gate and from gate to the baggage claim area. Genç, Erol, Eksin, Berber,
Chapter 2: Literature Review 35
and Güleryüz (2012) employed a hybrid of heuristic and stochastic approaches to
minimise the total duration of un-gated flight. This can be achieved by reducing the
total distance that all passengers walk including:
Connection flight travelling distance
The maximum distance that a passenger needs to walk in total
Similarly, Ding, Lim, Rodrigues, and Zhu (2005) used the Tabu search method
to investigate the problem when the number of aircraft exceeds the number of available
gates. The two main objective functions of this work were to minimise the number of
un-gated aircraft and to minimise total walking distance.
Barnhart, Fearing, and Vaze (2014) developed a multinomial logit model to
address the issue of disaggregate passenger itinerary flows using a small set of
propriety booking data. The authors developed a simplified regression-based approach
for estimating passenger delays. Passenger delays can be caused by three main factors
including flight delays, flight cancellations and missed connections. Delays are caused
by the following factors:
Distribution of flight load factors
Distribution of daily average load factors
Distribution average flight load factors by day of week and time of day
Distribution of percentage of connecting passengers
Distribution of connection time for one-stop passengers.
Recently, Jacquillat and Odoni (2015) developed an approach to interface
tactical capacity utilization which meant optimizing the utilization of airport resources
to process flights over a day. A strategic queuing model of airport congestion was
proposed, which meant planning flight schedules well before the day of operation and
taking into consideration long-term patterns of capacity availability. The main
objective of their study was to model the relationships between flight schedules, airport
capacity and flight delays at the strategic level through understanding of how flights
will be operated at the tactical level. Thus, the authors examined delays over the course
of day as a function of arrival and departure service rates, by means of a stochastic and
dynamic queuing model. The authors also formulated arrival and departure service
rates as a function of flight schedules, operating conditions and observed queue
36 Chapter 2: Literature Review
lengths. The purpose of this model of airport congestion was to quantify the magnitude
of delays and their evolution over a day as a function of flight schedules and airport
capacity. The model is strategic: it uses information that is available before a day of
operations. It may then be used to test the impact of changes in flight schedules or in
airport capacity on flight delays in support of airport congestion mitigation and airline
scheduling. Factors included were weather conditions, landing and take-off ratios and
runway configurations in use.
The research by Mujica (2015) is noteworthy because of the development of a
mix of two models to satisfy the different mandatory restricted policies relating to
airport terminal processing units, such as opening or closing check-in counters for each
flight, check-in starting time, and load balance. In the first phase, an evolutionary
approach was used to improve the initial allocation of check-in counters, taking into
account the policy restrictions. Once the best solutions were found, the author designed
a simulation model to determine which allocation was the most efficient in real life
situations, taking into account significant factors, such as the profiles of the travellers.
Similarly, Yan, Tang, and Chen (2014) addressed the perturbations of the check-in
process in airports by developing a zero-one integer programming model. The types
of perturbations considered in this study, however, occurred due to temporary airport
incidents, such as an airport closure, the crashing of the counter computers, and
temporary power failures. Yan et al. (2014) proposed solution methods that could be
used in the real world to resolve check-in counter reassignment problems. The
variables used were the length of time window and the number of service lines.
An optimisation issue in relation to staff scheduling problems in security services
was resolved by Abdoul Soukour et al. (2013) using a memetic algorithm (MA) with
concepts of an evolutionary algorithm and local search techniques. The algorithm
performs days-off scheduling, shift scheduling, and staff assignment together with all
specifications. Sigurðardóttir (2011) designed a mathematical model using mixed
integer programming, which provided a feasible solution for irregular staff schedules.
The model was tested for three different types of employee datasets, which were solved
using a local search algorithm. The model was able to serve under the conditions of
multiple and changing objectives and goals of staff scheduling and was flexible enough
to handle all the constraints and requirements of staff scheduling in terms of shift
length and shift start time.
Chapter 2: Literature Review 37
At Aarhus Airport, a heuristic method was used by Jensen (2015) to design an
algorithm that creates a staff schedule in a strategic way. The proposed algorithm was
designed for use with specific rules and regulation working hours. It was used to
provide the minimum number of handlers needed to fulfil the demands of each period.
The algorithm was tested to give the actual demand instead of a demand estimate
derived from existing schedules. Tang, Alam, Abbass, and Lokan (2009) presented a
multi-objective constrained resource allocation problem where the first objective was
to maximise the quality of service, and the second objective was to minimise the total
cost. A genetic algorithm was presented to allocate resources among the different
objects in an airport.
For the security domain, Skorupski and Uchroński (2018) presented basic
models that explain passengers with numerous strategies, security personnel with
various jobs and concerned queuing structures. Prior to formal modelling, it first
gathered actual data from Shenyang Taoxian International Airport (STIA). The authors
performed simulation analysis by distributing service times and arrival time intervals,
using three kinds of staff and compared the performance of various network structures.
The paper offers findings regarding establishing the satisfying structure of network
queuing for airport security check-points. It produced a new result demonstrating that
a blend of n M/M/1 systems will perform better than or equal to a M/M/n system when
considering feelings and strategies of passengers. A summary of the models used to
address airport optimisation problems is presented in Table 2-2.
38 Chapter 2: Literature Review
Table 2 - 3: Summary of models used to address airport problems
Modelling
approach
Topic Factors Performance metric Measurement Papers Case Study/ Validation
DES models Passenger flow
analysis
Identifying
bottlenecks
Security system
performance
Flight schedule
Demand fluctuations
Complexity of the system
Baggage volume
Alarm rate
Processing time
Waiting time
Average queue
Processing points utilisation rate
Number of opening counters
Throughput and PAX cycle time
Alodhaibi, Burdett, &
Yarlagadda (2017);
Dorton & Liu (2015);
Gronfula (2014); Guizzi
et al. (2009);
Kierzkowski & Kisiel
(2015); Novrisal et al.,
(2013)
Soekarno-Hatta International
(SHI) Airport.
Naples International Airport
Wroclaw Airport
Microscopic
simulation model
Dynamic
management
Security issues
Resource allocation
Analysing passenger
flow in new terminal
Flight schedule
Human behaviour
Walking speed
Availability of workers
Available capacity
Intensity of passenger flows
Similar to above
Average speed
Average duration of a
passenger’s stay in the system
Number of operator work hours
Beck, (2011);
Kierzkowski & Kisiel,
(2015, 2016); Schultz &
Fricke (2011); Siadat et
al. (2012)
Dresden Airport
Calgary International Airport
Heathrow Terminal 5
Wroclaw Airport
Simulation
System Dynamic
Performance
Evaluation
Passenger flow
Passenger transfer rates
Passenger arrival distribution
Capacity, delay, resource
utilisation,
Level of Service
Manataki & Zografos
(2009b, 2010)
Athens International Airport
Agent based
model
Passenger flow
Group dynamic
Passenger group dynamics
Passenger behaviour
Walking speed direction
Dwell time
Level of service (LOS)
Space Utilisation
Cheng, (2014); Cheng,
V. Reddy, et al. (2014);
Ma (2013); Ma, Fookes,
Kleinschmidt, &
Yarlagadda (2012); Ma
et al. (2011)
Brisbane International Airport
Queuing theory Interacting passenger
flow
Process delays
Performance measurement
Delay cost
Cycle time
Throughput per unit of time
Dorton & Liu, (2015);
Hsu et al. (2014);
Jacquillat (2012);
Jacquillat & Odoni
(2015)
Taoyuan International Airport
John F Kennedy Airport
Fuzzy logic
application
Control passenger
flow
Security efficiency
Efficiency evaluation
Flight Number
Available capacity
Number of available resources
Number of PAX handled
(Cheng, Mu, Zhang, &
Zhang (2014); Artur
Kierzkowski & Kisiel,
(2017); Mu et al.,
Wroclaw Airport
Calgary International Airport
Chapter 2: Literature Review 39
(2014); Skorupski &
Uchroński (2015, 2016)
Optimisation
techniques
Integer
Programming
Mixed integer
nonlinear
Program
Heuristics
Staff allocation
Airport facilities
planning
Efficient traffic flow
Delay approximation
Workforce demand
Passenger load factor
Fluctuated service demand
Flight schedule
Airport size
Flight schedule
Passenger arrival rate
Day-off scheduling
Staff scheduling
Staff assignment
Capacity utilization
Delay levels
Similar to above
Average arrival rate
Maximum flow rate
Abdoul Soukour et al.
(2013); Lin et al. (2015)
Sun & Schonfeld (2016)
Kim et al. (2013); Rodič
& Baggia (2017); Solak
et al. (2009)
----------------
---------------
Hartsfield Jackson Atlanta
International Airport
US hub airport
Chapter 2: Literature Review 41
2.7 SUMMARY OF THE REVIEWED LITERATURE
This chapter reviews the current problems that prohibit airports from efficiently
performing their operations. Passenger flow analysis and control has been the focus of
many publications to address the related issues that have a subsequent impact on
passenger flow, such as limited capacity, efficiency of airport systems, and congestion
problems. The second critical issue that challenges airport authorities is security which
results in an extensive literature regarding the security check process, the capacity of
security screening areas, and dynamic system management. Staff allocation problems
is another issue that has been studied over the past few years, taking into account staff
assignment, workforce demand, and days on-off scheduling.
The literature discussed different types of models that are used to understand and
resolve the recognised problems. These models can be categorised as analytical,
simulation, optimisation, and hybrid models providing decision support capabilities at
all levels of detail: from macroscopic, through mesoscopic, to microscopic. A
macroscopic approach can be used for capacity planning because it can provide
‘‘approximate answers to planning (primarily) and some design issues, with emphasis
on assessing the relative performance of a wide range of alternatives’’(De Neufville et
al., 2013). Additionally, macroscopic and mesoscopic approaches have been employed
to evaluate the effect of instance scheduling and resource allocation on performance
metrics such as queue length and waiting time. Microscopic models have been used to
simulate individuals’ interactions at a higher level of details. Wu and Mengersen
(2013) argued that macroscopic models are insufficient to handle the variability,
complexity and stochastic nature of airport terminals, but microscopic approaches are
difficult to deal with because they require large amounts of data for the high level of
detail.
The models were built based on a comprehensive set of parameters that
characterise the issues, such as flight schedules, processing time, service rates
distribution and number of resources, the facilitation process, and associated passenger
characteristics (e.g. nationality, as it influences which customs lane the passenger can
use).
42 Chapter 2: Literature Review
2.8 KNOWLEDGE GAP IDENTIFIED
Despite the great efforts undertaken in modelling and simulating the issues of
passenger flows, security and staff allocations, there are significant identifiable gaps
in the current knowledge of airport operations. First, while there is considerable
research aimed at modelling and simulating passenger flows at airports, there is limited
research investigating the impacts of different arrival patterns within airports. Second,
there has been little research on development of holistic models that provide an
integrated view of the processes and sub-processes of the whole airport which help
with analysis and evaluation of the various measures of the efficiency of the airport.
According to Zografos et al. (2013), most of the recent tools and models are only
focused on the individual process and address fragmented sections of the decision
making procedures of airports. Third, there have been few attempts to facilitate the
possible integration of airport outbound and inbound processes, including the potential
of the incoming passengers to draw significant personnel resources. Therefore, these
knowledge gaps were formulated as the research questions explained in section 1.2.
2.9 FORMULATION OF RESEARCH SCOPE AND RESEARCH
CONTRIBUTIONS
The motivation for this work was to develop a model capable of studying
passenger flows and staffing requirements at international airport terminals as a single
unit by facilitating the integration of outbound and inbound systems. More
specifically, this would involve:
Development of a simulation framework for outbound passenger flow using
ExtendSim V9.2 simulator software.
Investigating the effect of arrival patterns of departing passengers on the
departure terminal operations.
Development of advanced resource management algorithms to integrate
with the simulation model, including both outbound and inbound processes.
Development of an analytical optimisation framework to perform capacity
planning for strategic planning as the simulation model can only be used at
the operational planning level.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 43
Chapter 3: Simulation Model Framework
for the Outbound Passenger
Processes at an International
Airport
3.1 OVERVIEW
In the previous chapter, the background information regarding passenger flow
modelling was presented. To achieve the research aim of developing an overarching
model for the two systems (inbound and outbound), a comprehensive review of the
available literature in the fields of modelling airport terminal operations and
performance evaluation is required. This chapter introduces a generic framework for
an integrated simulation model of departing passenger flows as the first stage of the
overarching model. Figure 3-1 illustrates the common layout of an international airport
terminal and focuses on the standard outbound processes, such as check-in, security,
immigration, and boarding, and the standard inbound processes, including
disembarking, baggage claim, immigration, and quarantine. The first step towards the
development of the final model, however, is a simplified scheme consisting of only
the left-hand side of Figure 3-1 (i.e. without the interaction with the inbound
passengers).
The main objective of this research was to develop a model that can accurately
identify bottlenecks and improve operational efficiency. This model can also evaluate
the effect of an increasing number of passengers on the terminal facilities, a factor that
has made airport systems much more complicated. As a result of this rapid growth in
the number of air travellers and the complexity involved, numerous regulations and
new technologies are being applied to airport operations (Ma, 2013). For example,
flight schedules are frequently changed due to irregular demand. Therefore, a
simulation has been selected as the desirable approach to fully understand the complex
system of an airport. In addition, a wide range of what-if scenarios can be explored
throughout the model to assist in more effective decision-making during airport
terminal operations’ planning, design, and management.
44 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 1: Overview of an airport’s terminal processes, including outbound and inbound processes
3.2 THE CONCEPTUAL FRAMEWORK
In this section, a generic framework for modelling the flow of passengers
through the airport terminal processes is introduced (see Figure 3-2). Each system has
its own particular flow and each system requires a different infrastructure and services.
The development of a model for an airport comprises the following tasks. The first is
the development of a general international terminal system for the outbound traffic
system. The second task is the evaluation of the demand and supply of an airport
terminal. In this step, the entities of the general international terminal system are
established and the characteristics and capabilities of the model are evaluated. The
required information in this step are flight schedules, the structure of the terminal, and
implementation information.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 45
Figure 3 - 2: Airport system model.
The third task comprises the development of an algorithm approach for
managing the resources of the airport system and the fourth task involves validating
and checking this model. The models require validation to ensure their consistency and
credibility and both factors are equally important for any model. The fifth and final
task is the application of the model to various scenarios for further analysis. The model
can be successfully applied to different scenarios and cases related to an airport
terminal, such as arrival distributions, processing time distributions, and the different
internal and external structures of an international airport terminal (Chiu & Walton,
2002). During the fifth step, Key Performance Indicators (KPIs) are monitored as part
of the application of the above scenarios. The KPIs for this model include the average
waiting time, maximum waiting time, average queue length, and maximum queue
length. The basic international airport system model is depicted in Figure 3-2.
As mentioned above, airport systems are extremely complex, therefore, a multi-
disciplinary approach was utilised to understand all the flows and associated features.
The application of simulation techniques is highly beneficial for airport operations
management, especially for an airport’s own standard processes. To obtain a unique
model, the simulation of the system must be constructed according to the specifications
of the specific model that is being studied. The proposed model can determine
bottlenecks in the system and alternative procedures can then be attempted to optimise
the system without negatively impacting it.
46 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
3.3 PASSENGER FLOW CHARACTERISTICS
The passengers in an airport terminal can be divided into three types based on
how they are handled inside the terminal: departing passengers, arriving passengers,
and transferring or transiting passengers. Each type of passenger behaves differently
according to why they are using the airport’s facilities. An Australian international
airport terminal has been used as an example to demonstrate a common layout of
airport terminals. According to Ma (2013) definition, departing passengers start with
the check-in process in the international terminal and transfer to their airplane within
the same terminal. These passengers arrive at the airport terminal according to their
flight schedule and normally arrive at least two hours before their departure time.
Departing passengers complete the three main processes: check-in, security screening,
and immigration, and then wait to board the airplane at their specified gate. In contrast,
transferring passengers merely pass through the security screening control and then go
directly to their specified boarding gate as shown in figure 3-3.
Figure 3 - 3: Airport outbound processes (Shuchi, 2016).
The other type of passenger flow is that of arriving passengers, who can be
defined as people who disembark from the aircraft after landing at the airport terminal.
The difference between the process that arriving passengers go through and that for
departing passengers is that the latter is more complex. It involves services provided
to transit passengers and the time taken to complete this process is often significantly
longer than that of the arrival process (Odoni & de Neufville, 1992). Inbound
passenger flow is smoother than outbound passenger flow, although delays can occur
Arrival to the Terminal
Check-inSecurity
ScreeningImmigration and Custom
Boarding
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 47
in the inbound system depending on the time it takes to deliver baggage from the
aircraft to the baggage claim area (Ma, 2013).
The current study is predominantly concerned with the airport operations
associated with departing (outbound) passengers. Inbound passenger flow has the
potential to significantly affect the operations concerning outbound passengers. For
example, airport resources may be allocated to services for inbound passengers, e.g. in
the customs area. As a result, additional delays and bottlenecks may affect outbound
passengers because fewer personnel are available to process them.
3.4 OUTBOUND PROCESSES MODELLING
Flight attributes is one of the most vital components of model simulation and
refers to the information necessary for the establishment of the outbound system. There
are three divisions in flight attributes: flight schedules, passenger characteristics, and
boarding characteristics (see Figure 3-4). Outbound passenger attributes can be
generated via the following procedures. The first step involves using flight attributes
to consider the related information, such as flight schedules, check-in types, arrival
methods, and travel class (see Figure 3-4) and storing this in an Excel spreadsheet.
The second step involves developing an algorithm to generate the departing
passengers’ attributes. This is can be done using the Excel programming language
Visual Basic for Applications (VBA). Flight attributes are used as an input for this
algorithm as illustrated in Figure 3-5. The next section discusses the process of arriving
at the airport.
48 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 4: The input modelling of an outbound simulation model.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 49
Figure 3 - 5: Flowchart for generating outbound passenger attributes.
3.4.1 Arrival at the terminal
Depending on the purpose of travel, airport arrivals are divided into business and
leisure passengers in the developed model. Business passengers are those who are
traveling for work, for instance for a meeting or conference, whereas leisure
passengers are those who are traveling for a holiday or to visit family and friends.
Arrival patterns can be influenced by these two types of passengers (Cheng, 2014;
Manataki & Zografos, 2009b), for example, business travellers tend to use airlines
more frequently and are therefore more familiar with the workings of an airport
terminal and the reliability of the access mode (Ashford, Mumayiz, & Wright, 2011).
The main factor that influences passengers’ time of arrival at an airport is their flight
schedule, which varies from day to day and according to the day of the week.
Robertson, Shrader, Pendergraft, Johnson, and Silbert (2002) stated that depending on
the season, there may be more flights scheduled, e.g. in summer.
50 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Passenger arrival behaviour can be captured by showing the distribution of
passenger arrival times. The distribution shows the number of air passengers and the
time at which they arrive at an airport prior to the departure of a scheduled flight
(Kamyszek, 2014; van Boekhold et al., 2014). The flow rate of passengers arriving for
an international flight provides a universal arrival pattern. In accordance with the
example provided by Ashford et al. (2011), for the accumulative arrivals of passengers
before the scheduled time of departure, all passengers had arrived by one hour before
the scheduled departure time of an international flight. For local flights, however, all
passengers had arrived by 20 minutes before the flight was scheduled to depart (see
Figure 3-6).
Figure 3 - 6: The relationship between departing passengers’ arrival times and the type of flight
(Ashford et al. 2011).
There are many factors that affect an airport’s arrival patterns, e.g. airport ground
access, security issues, and the situation regarding traffic and transportation modes
(Ashford et al., 2011; Manataki & Zografos, 2010; Stefanik, Kandera, & Badanik,
2012). Furthermore, the arrival pattern of an airport will differ at different times of the
day (Stefanik et al., 2012). Rauch and Kljajić (2006) noted that passengers with early
flights generally arrive later than the statistical average. Many scholars hold the view
that the arrival behaviour of international passengers is common for most airports.
Firstly, almost all air travellers arrive one hour before the scheduled departure time of
an international flight. Secondly, the peak hours of the check-in procedure for each
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 51
flight normally occur between 100 and 120 minutes before the scheduled departure
time of a flight. Thirdly, business passengers arrive later than leisure passengers.
Lastly, the peak hours earlier on in the day are busier and shorter than the peak hours
in the evening and afternoon (Ashford et al., 2011; Cheng, 2014; Chiang, 2011; Chiu,
2002; Stefanik et al., 2012). Passengers arrive at an international airport terminal via
different transportation modes, e.g. trains, public buses, taxis, and private cars (which
they park at the airport for either a short or long term) (Manataki & Zografos, 2009b).
The model’s input will be discussed in the sections that follow.
3.4.2 Check-in module
In this section, the check-in modelling processes is explained in more detail,
including the check-in characteristics of passengers and the physical environment.
Figure 3-7 shows the main processes used in the simulation. The simulation model
considers three ways that the passengers can check-in. The first is the traditional way,
where the passengers are processed at check-in counters. This method can be divided
into two types: specific check-in and common check-in.
Common check-in refers to counters that can be used by different flights for the
same or different airlines, while specific check-in refers to the counters that can be
used by different flights for just one airline. Another type of check-in is self-service,
which consists of both types of auto check-in: kiosks and online check-in. Most airlines
provide an online check-in service to reduce the processing time for passengers.
52 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 7: Flowchart of check-in processing at international airport.
The model also considers bag-drop facilities for passengers who checked in
online or for those who check in using self-service kiosks. Delay times at the check-in
area are influenced by significant factors, such as the number of bags for each
passenger. It is assumed that the distribution of the number of bags for each passenger
is uniform (0, 2) (Ma, 2013). The delay time that can occur during check-in can be
calculated by the formula (Park & Ahn, 2003):
𝐷𝑒𝑙𝑎𝑦 𝑡𝑖𝑚𝑒 𝑎𝑡 𝑐ℎ𝑒𝑐𝑘 𝑐𝑜𝑢𝑛𝑡𝑒𝑟𝑠 = 0.2 𝑚𝑖𝑛 ∗ # 𝑜𝑓 𝑏𝑎𝑔𝑠 (1)
The structure of the check-in module is built based on a hierarchical model,
starting with a high level of check-in processes and moving down to sub-processes and
sub-sub processes (see Figure 3-8).
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 53
Figure 3 - 8: Module hierarchy of check-in system.
Passengers are advised to be at an international airport 2.5 hours before their
departure time and check-in closes 25-30 minutes prior to the flight’s departure
(Cheng, 2014). In the model, self-service check-in was built in the area located close
to the check-in counters. Sets of check-in counters were then created to serve either an
airline or a group of airlines. These sets consist of a row of 20 counters for check-in
services. Among the 20 counters, there are three counters for business class and
seventeen for economy passengers. The model is dynamic over a set time and can run
various scenarios, such as number of check-in counters needed, which can be
controlled by the input data represented by assigned. The major elements and
processing facilities of the check-in module are summarised in Table 3-1
The above flowchart maps out the data entities and relationships for a check-in
system and then translates this into an ExtendSim model that involves the following
options:
Passengers who check in online and in the terminal.
Passengers with and without baggage.
Economy and business passengers (with business priority).
54 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Passengers with and without baggage problems to be fixed during the check-
in process.
Passengers who only need to drop their baggage off after checking in online.
Table 3 - 1: Summaries of major elements and processing facilities of check-in module.
Elements and processing facilities Values
Check-in type
0 = online check-in
1 = airport counters
2 = self-service
Distribution of number of bags
Uniform (0,2)
Percentage of business class passengers 15%
Percentage of economy class passengers 85% (Cheng, 2014)
3.4.3 Security screening module
In security screening, the main activities are x-rays and carry-on luggage scans.
Passengers place their belongings, including laptops, liquids, and personal items into
separate trays with their take-on bags for scanning. All passengers and their carry-on
bags must be checked (see Figure 3-9). Each passenger joins the security checking
queue and when they reach the head of the queue, they are guided by security staff to
an available counter. Items are placed onto the x-ray machine and passengers walk
through the metal detector. Passengers and carry-on bags must successfully pass the
metal detector and x-ray examination, respectively. There is the possibility of failures
for both passengers and bags, however. If a passenger fails the metal detector check,
they are asked to go through the metal detector again or are selected for a body check.
The required data related to time statistics was collected (Philip J Kirk, 2013a),
such as time spent at security screening processes. On average, the time spent at
security screening is between six and seven minutes and 10% of all passengers fail
when they first walk through the metal detector. Assumption was made that 15% of
the total passengers have been chosen arbitrarily for the random explosive trace check.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 55
Figure 3 - 9: Flowchart of screening checkpoints for processing at the international airport.
The elements of security screening checkpoints are consistent from one
checkpoint to another. These elements are classified as static in nature, such as the
footprint required for a WTMD, TRX, AT, or WBI. Other elements, such as the
passenger queue and composure area, are variable because the characteristics change
based on the space available, airline passenger load factors, and the number of
passengers screened at checkpoints per hour (Transportarion Security Administration,
2009).
3.4.4 Immigration module
The immigration process is the third stage of the departure process and occurs after
the security control process. Figure 3-10 illustrates the immigration operation
56 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
processes considering the two methods of document checking-in at Australian
international airports: Smart Gate and immigration counters. An assumption is made
that 30% of passengers used Smart Gate because of the strict rules that have been
enacted by the Australian Border Force (Australian Government, n.p. ). The rules are:
The age of the passenger must be 16 years or older.
Travellers must be from one of the following countries: Australia, Canada,
China, France, Hong Kong, Ireland, Japan, Korea, Macau, New Zealand,
Singapore, Sweden, Switzerland, Taiwan, United Kingdom, or United States
of America
Figure 3 - 10: Flowchart of immigration system at international airport.
The other passengers are manually processed at immigration counters. Like the
other standard processes, check-in and security, passengers join the queue before being
processed by personnel from immigration. In the model, the number of available desks
will be set up based on demand. Peak hours require more available desks to meet
passenger satisfaction. If the queue length exceeds the configured threshold value, an
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 57
additional desk is opened, i.e. to open one more desk the number of passengers in the
queue must have increased by 40.
3.4.5 Boarding procedure module
The last stage of the outbound process is boarding passengers onto an airplane
and the airplane leaving the airport. Airlines are responsible for boarding passengers
when the aircraft is ready to depart (Shuchi, 2016). Normally, gates are open 30
minutes before the departure time and passengers should present their boarding pass
and passport to be checked by airline staff at the gate.
In this module, an algorithm was developed to manage the boarding procedures
for all gates. ExtendSim’s C-based language ‘ModL’ was used to code the algorithm.
Each item going in represents a passenger arriving at the gate who immediately passes
through the block and out of the model. To code such an algorithm, it is very important
to understand the boarding procedures and declare the associated variables, including
constant and static variables. Another vital component of flight attribution is boarding
characteristics. The main elements of this component are boarding time, boarding
strategy, and gate number. We created custom blocks for managing the boarding
procedures for all gates. The passenger is tracked within the database global array
called 'boarding'. The counter in the value receives a value every minute so that
operations can be performed to track the queues and boarding status of each passenger
for all gates. For this research, there are two boarding strategies:
The nature of the first boarding strategy is unconstrained. In this strategy, it
is assumed that 20 people enter a plane within 60 seconds.
The nature of second boarding strategy is random. In this strategy, it is
assumed that 15 people enter a plane within 60 seconds.
3.5 EXTENDSIM MODELS FOR OUTBOUND PROCESSES
In this study, discrete event simulations of passenger flow and queuing in
different airport facilities (including between operational sections) were implemented
using the simulation software package ExtendSim. It helps developers to connect
blocks together in order to move items though the system from the beginning of the
processes until the item exits the system (Diefenbach, 2010). This software package
facilitates the simulation of passenger flow in the presence of multiple facilities within
58 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
an airport. This assists with the identification of any existing or expected bottlenecks
and passenger processing times, as well as with the optimisation of operational
capabilities and identifying the personnel resources required to minimise processing
times. In addition, this simulation package has the ability to program the assumptions
of a system via a variety of blocks and connections. The simulated arrivals into the
airport terminal can be generated in relation to the distribution that counterparts show
in real-life.
An excellent feature of ExtendSim is its ability to build models with menus of
blocks. These blocks can be pulled onto the model page and linked to other blocks and
their function is to control both the attributes and flow of items in the model. Another
significant feature of ExtendSim is that the developer can modify and even create new
blocks for the user using the ‘ModL’ language to attend to specific cases within the
model. The blocks are activated when entities pass through them, demonstrating the
operations that are performed in the system (Diefenbach, 2010).
The viability of this approach for airport simulation has been confirmed by the
large number of previous publications that have used this software package or a similar
software package to analyse and simulate various airport operations, including the use
of ExtendSim for the simulation and optimisation of airport baggage handling
Savrasovs et al. (2009) and security screening simulations van Boekhold et al. (2014),
the use of the Arena package for the DES of passenger flow in a terminal Guizzi et al.
(2009), the use of the SES/Workbench package for the optimisation of airport
operations Saffarzadeh & Braaksma, (2000), the use of the General Purpose
Simulation System for the simulation of passenger flow and airport capacity Rauch &
Kljajic, (2006), and the use of the AnyLogic package for the simulation of passenger
flows and security issues (Curcio et al., 2007).
At the same time, it has been argued that the real capabilities of the ExtendSim
software package for discrete event and discrete rate simulations and the optimisation
of airport operation processes are far from being fully explored. This is demonstrated
by the significantly more extensive and successful use of this package to simulate and
optimise the even more complex operational processes of hospital emergency
departments and other facilities (Diefenbach & Kozan, 2011; Diefenbach, 2010;
Dorokhov, 2009; Williams, Chambers, Dada, McLeod, & Ulatowski, 2014).
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 59
The output of the ExtendSim model includes the total number of passengers
processed by particular workstations and checkpoints in the airport terminal, the
number of staff involved in each operation or service, the number of passengers
currently (and on average) queueing for a particular operation or service, and the wait
time and processing time for all passengers (including the average and maximum wait
time and processing time). This output enables the identification of bottlenecks and
weak operational points that may cause major processing delays.
3.5.1 Hierarchical blocks
The models were built based on a series of hierarchical blocks, including the
arrival of passengers, check-in, security screening, immigration and customs, and
boarding. These hierarchy blocks contain a number of blocks that each have a different
functionality, including blocks that simulate the steps in a procedure (Queue, Activity),
blocks that perform a calculation (Math, Random Number), blocks that store data or
interface with other applications (Read, Write), and blocks that plot the result of the
model (Plotter, Histogram). There are also some tools for interface formation (Popup,
Buttons). The hierarchical blocks that are created can be stored in ExtendSim’s library
and reused again in the same or a different model.
3.5.2 ExtendSim modules description
As explained in the section above, the structure of the model is built around the
basis of a hierarchical model structure. In this context, the proposed model is organised
into two hierarchical levels:
1) The first level of the hierarchy reflects the airport departure system broken
down into a set of the main departure procedures, including check-in,
security, immigration, and boarding.
2) The second level describes the intricate details of the different sub-processes
in the airport terminal. Specifically, the main departure procedures that the
airport terminal model consists of are:
arrival characteristics, including the distribution of arrivals, method of
arrival (car, bus, or train), number of bags, class of travel, and time of travel;
60 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
the check-in process, including the type of check-in, e.g. at the kiosk or
online with a bag, business or economy, and the assignment of each flight
to specific check-in counters;
security screening, including x-ray checks conducted in the common
security screening line, x-ray checks for diplomats, and secondary screen
checks (i.e. random checks);
immigration processing, including Smart Gate services and the manned
counter for passport control; and
boarding procedures, including boarding time, waiting time at the gate,
boarding strategy, jetway capacity, and flight capacity.
The simulation involves eight blocks that demonstrate the actual processes in
Australian international airports. Each block is explained in more detail in the
following sections. First, creating the entities in the model representing departing
passengers. These passengers are generated following the normal distribution
according to flight schedules. The data were stored in the Excel spreadsheet and then
linked within ExtendSim by the module called create (see Figure 3-12).
Figure 3 - 12: Input data represented by passenger attributes.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 61
Secondly, the values of the attributes of each passenger are defined in the
ExtendSim schedule table. Every passenger created as a simulation entity is tagged
with a unique attribute, such as arrival time, check-in group, and arrival method. Each
row of the table introduced above refers to Extendsim entities (passengers), including
the related attributes of the outbound processes. Some passengers arrive late to the
airport and are assigned high priority, meaning they can skip the queue if the queue
time is greater that the time until check-in closes.
Block 3 introduces the overview of the check-in system, starting with kiosk
check-in and followed by a hierarchy of blocks of check-in groups. Block 4 simulates
the check-in counters for both business and economy passengers. For the security
screening lanes, each lane includes x-ray machines to scan and check if there are any
dangerous or prohibited items; these are demonstrated in block 5. Block 6 simulates
the random checks where some passengers are selected randomly for further checks.
Block 7 simulates the immigration counters, including the SmartGate option. Finally,
passengers complete the outbound processes and are directed to the boarding
procedures located in block 8 that allow the passengers to exit the system.
Block 1: High level of outbound processes
The first block, ‘Block 1’, presents the outbound processes of international
airports and is hierarchical to simplify the complex processes that contain processes
and sub-processes (see Figure 3-13). For example, as explained in section 3.4.1, the
check-in system consists of three levels of processes: check-in groups for individual
or multiple airlines, check-in type, and check-in counters. The yellow blocks at the top
of Figure 3-13 refer to the input data of the model. ExtendSim data can be classified
into three groups:
Passenger attributes, such as SmartGate users, etc.
Operational data, such as average processing time, number of available
resources, etc.
Flight attributes and related information, including gate number, departure
time, boarding time, etc.
62 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 13: Block 1: ExtendSim simulation for outbound system.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 63
Block 2: Passengers with high priority
Block 2 consists of two modules. The first module creates passenger entities
using the schedule, while the second module aims to identify passengers who are
waiting at a common check-in queue and may miss their flight. Some flights might
share the same check-in counters because they are from the same airline. Therefore,
an algorithm was developed to allow passengers to skip the queue if the estimated
queue wait time is greater than the time until check-in closes.
Figure 3-14 demonstrates the technique for the proposed module of prioritising
travellers. Step one uses ‘Get block’ to read the flight number and class of travellers
to determine which check-in group they are in and the queue length. The next step
links ‘Get block’ with the developed customs block, ‘Prioritise arrivals’, to run the
algorithm illustrated in Figure 3-15.
Figure 3 - 14: Block 2: Prioritise arrivals.
Where:
𝑋 = the time before check-in counter close
𝑌 = estimated wait time at queue of check-in 𝑗
𝑄 = queue length of check-in 𝑗
64 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 15: Block 2: Algorithm for assigning passenger high priority.
Block 3: Check-in group simulation model
As there are three types of check-in, a decision block named ‘Select Item Out’
was used to read the check-in option for each passenger. The online check-in option
was assigned a value of 0, while common check-in (airport counters) and self-service
(kiosk) check-in were assigned values of 1 and 2, respectively (see Figure 3-16). After
identifying the check-in option, each item is directed to the associated option. The
reason for using a decision module in the check-in system simulation was because of
the location of a kiosk that comes before the common check-in counters.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 65
Figure 3 - 16: Block 3: Assigning check-in type using the decision module.
Figure 3-17 illustrates the self-service check-in module as the second module of
block 2. In this module, it is assumed that the processing time follows a normal
distribution where the mean is 2 and the standard deviation is 1. The capacity of this
module can be controlled using the ‘maximum items in activity’ box, which based on
the real data was 50 kiosks.
66 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 17: Block 3: Self-service module.
The last module of block 3 is where passengers are routed to the proper check-
in lane (see Figure 3-18). This can be done by determining which group the flight
belongs to. In the next section, the hierarchical block of check-in airline groups is
explained in more detail.
Figure 3 - 18: Block 3: Hierarchical block for check-in group module.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 67
Block 4: Check-in counters
Each check-in lane has an ID that is linked to the input data, including flight
attributes and passenger attributes. Another reason for giving each lane an ID value is
so the output of the simulation runs can be reported. Compared with other outbound
processes, the check-in system behaves differently because it is operated by different
airline companies within a separate lane, whereas security screening and immigration
are operated by the same agent for all lanes and counters.
Before entering the check-in counter queues, a decision module is used to read
the travel class of the passengers to route them to the proper queue (see Figure 3-19).
Figure 3 - 19: Block 4: Decision module for selecting class of travellers.
A decision module was also used to identify the attributes of check-in type with
respect to bag check-in. The check-in options at the common check-in counters
include:
online check-in with bags
self-service check-in with bags
common check-in with or without bags
Additionally, Figure 3-20 demonstrates the decision modules for the check-in
type and the number of bags for each passenger. Some passengers check-in using self-
68 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
service kiosks or online services, but they might have bags they need to check in
manually at check-in counters.
Figure 3 - 20: Block 4: Decision modules for number of bags and check-in type.
The last module of block 4 is the check-in counters module. An activity block is
used to simulate each workstation and how it works based on queue length. This
module consists of three sub-modules. The first module, ‘Get block’, is used to link
the input data stored in the Excel spreadsheet with the activity block to read the delay
time for each passenger based on the number of bags (see Figure 3-21).
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 69
Figure 3 - 21: Block 4: Delay time module.
The second sub-module of the check-in counters controls the workstation
dynamically by setting the workstation to ‘active’ or ‘shutdown’ based on the
availability and queue length (see Figure 3-22). This procedure can be done by using
a ‘read’ block to check if the queue length exceeds the threshold and if there is
available staff at each time unit of the simulation. The last sub-module of the check-in
counters uses a ‘writing’ block to report the utilisation of each workstation.
Figure 3 - 22: Block 4: Workstation control module.
70 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Block 5: Security screening checkpoints.
In this section, block 5 of the security screening system is explained in more
detail. The block considers the following modules:
VIP lane option as the first module
Queue system in front of x-ray machines
Workstations refer to (x-ray) machines
Recheck because of the first failure
Control workstation dynamically
Figure 3-23 illustrates the decision module of passenger type at the security
screening checkpoints. This module is used to route passengers to the proper security
screening lane. As 0.05 of passengers are diplomatic, block 5 includes a separate lane
for diplomatic passengers.
Figure 3 - 23: Block 5: Diplomatic decision module.
The passengers are then held in the queues for the x-ray lane for screening
purposes and are processed based on a first-in-first-out (FIFO) queue method (see
Figure 3-24) (van Boekhold et al., 2014). The output of the simulation, e.g.
maximum/average queue length and maximum/average waiting time at screening
machines is stored using a ‘write’ block.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 71
Figure 3 - 24: Block 5: Queue system module.
While Figure 3-25 shows the distribution of the delays at the x-ray machines, it
is assumed that processing time at x-ray machines follow triangular with maximum
0.75, minimum 0.2, and most likely 0.5 minutes (Cheng, 2014).
Figure 3 - 25: Block 5: Processing time distribution module.
Sometimes passengers, or their items, need to be re-checked because of a
triggered alarm. According to Olaru and Emery (2007), 10% of first screenings fail
72 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
and the items need to be re-checked. Therefore, the decision module illustrated in
Figure 3-26 was used to reroute items randomly.
Figure 3 - 26: Block 5: Security first failure module.
The last module of block 5 controls the workstations of the x-ray machines.
These processes can be based on demand, as represented by the queue length and the
available personnel (see Figure 3-27).
Figure 3 - 27: Block 5: Workstation control module.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 73
Block 6: Random explosive security check
After completing the x-ray stage, 15% of passenger will be selected randomly
for further inspection. Both diplomatic and non-diplomatic passengers can be chosen
to be inspected (see Figure 3-28).
Figure 3 - 28: Block 6: Probability of random explosive check module.
A decision module is used to appoint attributes to each passenger that is selected
for a random or secondary check (see Figure 3-29).
Figure 3 - 29: Block 6: Random check decision module
74 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
As can be seen in Figure 3-30, the activity block is used to simulate the
workstation. The processing time distribution for this block follows the normal
distribution, with a mean of 2 minutes and a standard deviation of 0.2 minutes.
Figure 3 - 30: Block 6: Processing time distribution at random explosive check.
Block 7: Immigration counters and SmartGate system
The logic of block 7 can be separated into four identical sections. Each section
has its own features in Linking, assigning and managing the process of handling
passengers. Figure 3-31 shows the decision module as the first module of block 7,
which is used to identify the SmartGate users and then routes each type of passenger
to the SmartGate or to the common immigration counters.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 75
Figure 3 - 31: Block 7: SmartGate user decision module check.
Figure 3-32 shows an overview of the SmartGate service and the distribution of
the passengers being processed. The module contains three sub-modules: a queue
system module, blocks for the output report, and a workstation module. An activity
block was used to simulate the SmartGate machine using the related parameters, such
as processing time distribution and the number of available machines. ‘Read’ and
‘write’ blocks were used to record the simulation output, e.g. maximum/average
waiting time and maximum/average queue length.
Figure 3 - 32: Block 7: SmartGate processing time distribution.
76 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Other passengers are directed to the standard immigration counters. A queue
block is used as the first block to hold passengers in front of the immigration counters.
People are processed based on the FIFO queue method (see Figure 3-33).
Figure 3 - 33: Block 7: Immigration queue system.
The last module of block 7 controls the operation of the immigration
workstations. In this module, a ‘read’ block is used to link each workstation with the
resource pool (see Figure 3-34).
Figure 3 - 34: Block 7: Immigration workstation control module.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 77
Block 8: Boarding procedures
Block 8 has three modules with different functionalities. As the distance between
immigration and the boarding gates varies due to the location of each gate, a
mathematical block denoted by an equation block was used to calculate the walking
time from the immigration system to the boarding gate (see Figure 3-35). The module
was developed according to the following steps:
Define the value of ‘walking speed’ if we assume that the average walking
speed is between 3-5 k/h based on the age of the passenger.
The destination is assumed to follow a normal distribution with a mean of
𝜇 = 200 and a standard deviation of 𝜎 = 70.
Therefore, the walking time is calculated using the equation
𝑤𝑎𝑙𝑘𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 =(
Distance 𝑚𝑒𝑡𝑒𝑟𝑠
1000)
𝑤𝑎𝑙𝑘𝑖𝑛𝑔 𝑠𝑝𝑒𝑒𝑑∗60 (2)
Figure 3 - 35: Block 8: Walking time distribution for boarding gate module.
The second module of the boarding procedure uses ‘Get block’ to read the
attributes of the flight number assigned to the right gate (see Figure 3-36).
78 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 36: Block 8: Walking time distribution to boarding gate module.
ExtendSim allows modellers to develop custom blocks to mimic real-life
scenarios. Therefore, an algorithm was developed to simulate the boarding procedures
(see Figure 3- 37). In contrast to the other processes, the boarding process is performed
by the same staff as the check-in process, therefore, there is a need for an algorithm to
share staff between check-in and boarding. The boarding procedure algorithm was
based on the following algorithmic steps:
1. Read each gate status.
2. Select which one is active based on flight attribute ‘boarding time’.
3. Do not select staff who have finished their shift.
4. Check if there is staff available in the pool, otherwise move staff from
associated check-in.
Figure 3-37 shows the algorithm flowchart for the boarding procedures. The
related data are set-up and uploaded at the beginning of the simulation. The input data
for the developed algorithm are ‘time taken to process at the gate’, ‘number of staff at
the gate’, ‘process time to get on the plane’, and ‘delay time on the jetway’.
Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 79
𝑁𝐴𝑆: Number of airline staff available in the database of global array
𝐴𝑆𝐶: Airline staff for check-in counters assigned 𝑗 = 1,2,3 …
𝐺𝑖: Departure gate 𝑖 = 1,2,3 …
𝐴𝑏𝑔𝑎𝑡𝑒: Airline staff for boarding gate
𝐴𝑆𝑠ℎ𝑖𝑓𝑡__𝑒𝑛𝑑 : End shift of airline staff
Figure 3 - 37: Block 8: Flowchart for boarding procedure algorithms.
3.6 NUMERICAL TESTING
The simulation framework was applied to Brisbane International Airport (BNE).
A model was developed that includes the main characteristics of the BNE with regards
to passenger flow and processing and with respect to a variety of functional areas and
facilities. To validate the model, four different load factors were evaluated. The load
factor is the proportion of an airplane’s seats that are occupied. The load factors
considered were 50%, 60%, 75%, and 100%. Several flight schedules were analysed
to understand their impact on passenger arrival profiles and terminal facilities.
80 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
3.6.1 Impact on arrival process
The arrival pattern of passengers and the rate of passenger arrivals is affected by
flight departure times and the destinations of flights. According to Rauch and Kljajić
(2006), passengers with early flights generally arrive later than the statistical average.
The mode of arrival to the terminal depends on the modal split, which is the proportion
of passengers that use private cars, trains, and buses. Figure 3-38 and Figure 3-39
demonstrate the passenger arrival profiles per mode of transport and show the
distribution of passenger arrivals over time (in 10-minute intervals). We assume 75%
of the BNE passengers use private cars, 10% use buses, and 15% use trains.
Figure 3 - 38: Arrivals patterns for 100% flights full.
Figure 3 - 39: Arrivals patterns for 50% flights full.
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Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 81
It can be seen that two high peaks occur in the morning at 08:20 and 10:30 and
that two lower peaks occur in the early evening and at night between 17:40 and 18:35
and between 21:20 and 23:50, respectively. This information can be used to assist
airport operational managers with scheduling staff within the terminal and its facilities.
It can also be used to determine the walking time required for the passengers from each
transport station at which they arrive at the terminal.
3.6.2 The impact on terminal facilities
As far as the efficiency of the operational processes at airport terminals is
concerned, Figures 3-40 and 3-41 and Figures 3-42 and 3-43 show the simulation
outcomes for the security screening and immigration processes, respectively.
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Figure 3 - 40: Security queue length 100% flights full.
Figure 3 - 41: Security queue length 50% flights full.
82 Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport
Figure 3 - 42: Immigration queue length 100% flights full.
Figure 3 - 43: Immigration queue length 50% flights full.
What-if scenarios were performed to analyse the queue length for the two
facilities. It is clear that the queue length for security screening decreases by more than
four times if the capacity of the flight is 50% full. For the same condition, the queue
length for immigration sharply decreases from 275 to eight passengers. In addition, the
above figures clearly demonstrate that a severe bottleneck occurs at security screening
and immigration during the day between 11:30 and 16:30. Therefore, it is believed that
the results of the model are quite accurate because they align with the arrival profiles
shown earlier.
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Chapter 3: Simulation Model Framework for the Outbound Passenger Processes at an International Airport 83
3.7 CHAPTER SUMMARY
At the beginning of this chapter, the three different types of passenger flow
processes were discussed: departure, arrival, and transfer. Each type indicates one form
of passenger flow. The conceptual framework for developing such a holistic model
that considers the entire processes of an airport terminal was also discussed.
This chapter focuses on outbound processes as the first component of an
overarching model for simulating passenger flow at Australian international airports.
ExtendSim simulation of outbound processes consists of eight key modules including
arrival to the terminal, check-in, security screening, immigration and boarding
procedures. Then the process of generating passengers arriving at the international
airport was explained using the flight attributes and the relevant information, such as
flight schedules, passenger characteristics, and boarding characteristics as the main
inputs.
The simulation results demonstrate that flight schedules have a large impact on
passenger flows. The proposed simulation framework and model can be used to predict
ahead of time the effect of different flight schedules and may be used as a feedback
mechanism to improve it before implementation. Taken together, these results suggest
that integrated flight schedule creation and passenger simulation analysis may be an
avenue for addressing some of the issues of passenger flow within airport terminals,
especially for the two most-affected processes: security screening and immigration.
The next chapter demonstrates how the developed outbound simulation model
can be used to analyse the problem of different arrival patterns and their impact on
departure terminal facilities.
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 85
Chapter 4: The Impacts of Arrival Patterns on
Airport Mandatory Processes
4.1 INTRODUCTION
Modelling of generic framework simulation models for outbound major standard
processes of an international airport terminal was the focus of work undertaken in Chapter 3.
The traditional layout of the outbound processes, the characteristics of entities data, and the
characteristics of operational procedures were discussed.
This chapter discusses the development of the passenger arrival process model and
demonstrates how the arrival patterns affect the ability of outbound processes to process
outgoing passengers (Joshi, 2008). Estimating the exact arrivals of passengers to the airport is
difficult as the passenger’s arrivals are highly dynamic, and the rates of arrivals change from
time to time. Hence, this study has been conducted to understand such uncertainties by testing
various shapes of distributions in order to provide the best policy that satisfies check-in,
security screening, and immigration processes in terms of minimum waiting time and length
of queue. The pattern of passenger arrivals is considered an important factor in planning
airport-terminal facilities, such as the number of check-in counters and service agents, along
with the operation times of passenger check-in and queue length (Park & Ahn, 2003). The
arrival pattern of passengers and the rate of passenger arrivals are affected by many factors,
including flight-departure times, type of traveller (business/leisure), and flight destination.
According to Rauch and Kljajić (2006) passengers with early flights generally arrive later than
the statistical average. The airport check-in rules are considered significant factors influencing
passenger-arrival patterns at the airport (Manataki & Zografos, 2009b).
Most airports share some common arrival behaviours for passengers in international
terminals. These behaviours are as follows:
90% of passengers arrive at the airport 60 minutes before departure time;
Business passengers arrive later than leisure passengers;
The peak hours of check-in are 100–120 minutes before the departure time; and
In the morning, the peak hours are shorter but busier than in the evening (Ashford et
al., 2011; Cheng, 2014).
86 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
The simulation model presented in Chapter 3 will be used to analyse the effect of
passenger arrival variation. This model also investigates the influence of arrival patterns on the
efficiency of airport-terminal processing and focuses on mandatory processes, including check-
in, security checkpoints, and immigration. The outcome of this analysis could help in setting
the time-of-arrival policy for international airports. To determine the most suitable arrival
policy for an airport, two basic parameters are critical: the mean value of time before flight (µ)
and the arrival time before flight (Ω). Two experiments were conducted to investigate the
influence of these parameters on arrival patterns and airport terminals at check-in points.
Finally, we present the strategy for determining the optimum policy for the airport in section
4.5.
4.2 DEVELOPMENT OF PASSENGER ARRIVAL PROCESS MODEL
Passenger arrival time is treated as a random variable. The distribution of passengers
arriving at the check-in counters varies by time of day, day of week, airport, season, and type
of passenger. Other factors, such as the mode of transportation and the security requirements,
are not considered Furthermore, this model explains the behaviours of any passenger (business
or leisure). The flight schedule for a Wednesday was obtained from an international airport and
used as input for the model to determine the following:
Estimate the volume of passengers showing up at the terminal.
The passenger-arrival pattern during the day (24 h).
Several different statistical distributions can describe the arrival profile based on the
given flight schedule. These distributions include the exponential distribution, uniform
distribution, empirical distribution, and normal distribution (Fonzone, Schmöcker, & Liu,
2015; Olaru & Emery, 2007; Schultz & Fricke, 2011). In this study, the normal distribution
was selected to characterise the passenger-arrival pattern based on the test done by Ma (2013)
for a given flight schedule, the whole arrival rate of the proposed data follow normal
distribution. Figure 4-1 depicts the flow chart used to obtain the arrival pattern under normal
distribution from the flight-schedule data.
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 87
Figure 4 - 1: Flowchart for modelling passenger arrivals at the international terminal
The modelling procedure for estimating the volume of passengers arriving at the airport
over time is based on the following algorithm:
1. Obtain flight schedule of the airport, including the related flight information that
contains the airline, scheduled time of departure, and number of passengers on each
flight.
2. Select the policy for passenger-arrival time at the international airport. For example,
2 hours, 3 hours, or 4 hours beforehand (scheduled departure time).
3. Determine the relevant distribution, along with critical parameters such as mean and
standard deviation.
Obtain airport flight
Select time of PAX arrive before flight
departure time
Calculate PDF for flight 𝑖
Calculate CDF for flight 𝑖
𝒊 = N
𝑖 = 𝒊 + 𝟏 𝒊 < N
Collect aggregation for all
88 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
4. Calculate the probability distribution function (PDF) and the cumulative distribution
function for each flight (𝑖) to determine the number of passengers arriving per time
interval before the departure time.
5. Add the number of passengers arriving at each time interval for each flight to
estimate the incremental total number of passengers arriving for all flights. Thereby,
the aggregate numbers of passengers arriving per time interval can be obtained for
the entire flight timetable.
4.3 CASE STUDY 1: IMPACTS OF DIFFERENT TIME BEFORE DEPARTURE
VALUES
In this case study, a multitude of scenarios has been tested. As explained previously, the
investigation will consider the two critical parameters, the mean value (µ) of arrival
distributions and the policy on time to arrive at the airport (such as 3, 3.5 or 4 hours before
departure time). The experiments are conducted to study the impact of different arrival times
at the airport given fixed µ (see Table 4-1).
The behaviour of Cumulative Distribution Function (CDF), and arrival profiles caused
by varying time before the flight (Ω) for a given mean (µ) time before departure will be
discussed. It also investigates to what extent different policies impact the performance of
terminal processing points.
Table 4 - 1: Selection of Ω values under a fixed µ values
Mean
(min)
Sets of time before departure (in min) under
each mean
60 120 150 180 210 240
90 120 150 180 210 240
120 120 150 180 210 240
150 150 180 210 240
180 180 210 240
210 210 240
For a given µ value, the CDF, and arrivals pattern, were calculated using the proposed
approach presented in section 4.2. This procedure was followed for every µ value listed in the
Table 4-1. The simulation software described previously was used to investigate the possible
effect on the passenger flow performance at terminal processing points by varying the time of
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 89
passengers arriving at the airport prior to departure time for a given mean value. The key
performance metrics, such as waiting time, average waiting time, queue length and average
queue length, have been considered for this analysis. These plots are described in the next
sections.
4.3.1 Behaviour of CDF of time before flight.
Figure 4-2 depicts the cumulative distribution function (CDF) for the same policies of a
passenger arriving for flight 𝑖. It is clear to see that passengers are more distributed under the
mean depending on how early they came to the airport, especially for those people arriving 2
hours and 2.5 hours before the departure time (see plots a and b). In contrast, there is less
distribution if passengers arrive 3.5 hours and 4 hours before departure time under the mean
value of 90, 120 and 150 min. This means passengers will experience less rush with these
policies. From Figure 4-2, it can be seen that any time after the mean value will have the lowest
CDF value corresponding to the earliest time before the flight. Also, it shows significant
variation in plots with low mean value and the variation decreasing as mean value decreases.
Figure 4 - 2: CDF of passengers arriving before flights for a given mean (µ): (a) µ = 60 min; (b) µ = 90 min; (c)
µ = 120 min; (d) µ = 150 min; (e) µ = 180 min (f) µ = 210 min.
(a) (b) (c)
(d) (e) (f)
90 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
For any given µ value, corresponding CDF curves for the time before flight values, share
a typical value at the mean. From Figure 4-2 it is also clear to see that for any given µ value,
in the time interval µ < t <= Ω, CDF increases as Ω increases and CDF decreases as Ω increases
in the time interval µ > t > 0. Hence, the impact from the Ω under same µ value is similar for
any µ value.
4.3.2 Behaviours of arrival pattern
Figure 4-3 illustrates arrival profiles of departing passengers for different times before
flight Ω under a given mean µ. For any given µ, the arrival pattern is very similar for any Ω
value that has the condition Ω>µ. Despite the merely slight differences for this situation, it can
be observed that the arrival pattern with different Ω values is almost similar, irrespective of the
µ value. However, for the graphs Ω≤µ, the arrival pattern shows variation from other graphs in
certain regions as follows:
i. For µ ≤ 120, considerable increase of PAX of can be seen in Ω = 120 curve, in the
time interval around 12:00 h to 20:00 h.
ii. For µ = 150, considerable increase of PAX of can be seen in Ω = 150 curve, in the
time interval around 07:00 h to 22:00 h.
iii. For µ ≥ 180, slight decrease of PAX of can be seen in Ω = 180 and 210 curves in
graphs (e) and (f) respectively, in the time interval around 14:00 h to 22:00 h.
4.3.3 Results of simulation and discussion
This section presents the data from running the simulation model to show the possible
impacts of varying time before flight under a given mean. Table 4-2 summarises the results of
the simulation model for the proposed scenarios, including four keys of the operational
performance metrics. These metrics are maximum/average queue length and
maximum/average waiting time. The simulation results illustrate that the queue length and
waiting time decrease when the time before a flight increases.
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 91
Figure 4 - 3: Departing passenger arrival profiles at airport terminal for different (Ω) under given (µ): (a) µ = 60 min; (b) µ = 90 min; (c) µ = 120 min; (d) µ = 150 min; (e) µ
= 180 min (f) µ = 210 min.
92 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Table 4-2: Detailed output of ExtendSim simulation model for case study 1
Scenarios Check-in Security Immigration
𝜇 values Ω value Max
queue
Ave
queue
Max
waiting
Average
waiting
Max
queue
Ave
queue
Max
waiting
Average
waiting
Max
queue
Ave
queue
Max
waiting
Average
waiting
𝜇 =60 120min 46 2.06 23.58 3.38 111 7.68 12.45 1.87 413 61.48 82.33 20.37
150 min 40 1.04 17.85 1.7 105 4.33 11.14 1.06 321 43.38 64.06 14.42
180min 40 1.14 15.29 1.9 126 7.07 13.04 1.73 344 47.66 68.66 15.91
210min 59 2.02 27.54 3.33 103 5.46 10.85 1.33 237 34.51 47.51 11.43
240min 95 2.95 37.61 4.87 54 1.76 5.36 0.43 220 20.85 43.93 6.86
𝜇 =90 120min 22 0.81 10.67 1.33 210 18.71 21.86 4.57 431 72.58 86.15 23.8
150 min 9 0.15 3.25 0.25 187 16.29 20.19 3.99 376 55.47 75.13 18.32
180min 14 0.31 6.16 0.51 138 9.35 15.11 2.27 353 54.84 70.51 18.34
210min 19 0.42 9.53 0.71 109 5.76 12 1.41 366 60.36 73.09 20.01
240min 34 0.56 14.03 0.92 49 2.88 5.26 0.71 295 47.22 58.88 15.58
𝜇 =120 120min 37 1.12 14.82 1.86 163 13.44 16.9 3.29 443 80.74 88.57 26.61
150 min 11 0.2 4.28 0.33 178 15.38 18.64 3.75 413 68.45 82.47 22.45
180min 7 0.08 3.92 0.124 181 13.34 19.86 3.25 408 70.36 81.69 23.19
210min 27 1.01 12.21 1.7 95 3.93 9.93 0.96 306 46.01 61.2 15.05
240min 16 0.12 6.21 0.2 125 8.3 13.28 2.03 330 49.12 65.77 16.33
𝜇 =150 150 min 11 0.11 4.03 0.19 140 9.85 15.28 2.41 389 65.82 77.69 21.89
180min 12 0.11 4.68 0.18 137 10.29 15.16 2.51 407 72.59 81.33 24.16
210min 4 0.03 1.48 0.04 127 9.63 13.13 2.36 371 62.97 74.22 20.68
240min 8 0.07 3.14 0.11 116 6.13 12.2 1.51 379 66.76 75.96 21.87
𝜇 =180 180min 8 0.04 3.24 0.07 163 11.31 18.19 2.77 359 58.21 71.68 19.63
210min 7 0.07 2.87 0.011 128 9.84 14.06 2.41 393 66.74 78.26 22.22
240min 31 0.7 14.2 1.16 121 7.59 13.06 1.86 382 64.3 76.22 21.21
𝜇 =210 210min 42 2.19 19.98 3.61 151 11.94 15.84 2.91 396 67.79 79.13 22.56
240min 69 3.21 27.59 5.27 78 3.56 8.94 0.087 294 46.93 58.49 15.61
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 93
As can be seen from Figures 4-4, 4-5, and 4-6, it is evident that for any µ and Ω
combinations, the overall behaviour of the three queues of check-in, security, and immigration
with different time before flight values are dissimilar for each µ value. However, the maximum
queue length for immigration for the same figures occurred at Ω = 120 min, while the minimum
queue length of immigration processes was found at Ω = 240 under the µ = 60 min. In the
check-in process, the maximum queue length happened at Ω = 240 min with µ = 60 min and µ
= 90 min.
Figure 4 - 4: Queue lengths of different time before flight given µ = 60
Figure 4 - 5: Queue lengths of different time before flight given µ = 90
94 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Figure 4 - 6: Queue lengths of different time before flight given µ = 120
From the above figures, it is observed that passengers who arrive with greater or equal to
120 µ values will be processed with minimum waiting in the queues overall outbound
processes. Moreover, there is a direct relationship between the policies of time of passengers
arrival at the airport and the performance of departure processes, where the waiting time in the
queue is decreased if the policy of time to arrive at the airport is increased.
4.4 CASE STUDY 2: IMPACTS OF DIFFERENT MEAN VALUES
The second experiment is focused on the impact of a variable mean value given a fixed
arriving time (such as three hours before departure time) as shown in Table 4-3. A combination
of different arrival policies at an international airport before the departure time was simulated.
The objective of case study 2 is to further investigate the behaviour of CDF, and the arrival
profile caused by different mean µ values for a given time before departure value Ω. For this
study, the following table was used to extract the scenarios. For a given Ω value, the CDF
departing passenger arrivals profile, we follow a similar procedure as that explained in case
study one, the proposed approach is used to calculate CDF and arrival profile. This procedure
was followed for every Ω value listed in Table 4.3.
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 95
Table 4-3: Selection of Ω values under different µ values
Time before
flight (min)
Mean (min)
120 60 90 120
150 60 90 120 150
180 60 90 120 150 180
210 60 90 120 150 180 210
240 60 90 120 150 180 210 240
For a given Ω value, the CDF departing passenger arrivals profile, we follow similar
procedure explained in the case study one, the proposed approach is used to calculate CDF,
PDF and arrival profile. This procedure was followed for every Ω value listed in Table 4.3.
4.4.1 Behaviour of CDF
Figure 4-7 illustrates the CDF of different mean values for a given time before the flight.
It can be seen that by having different mean values, the maximum values of CDF are similar,
and all graphs replicate each other. Moreover, for a given time before the flight, the CDF is
increasing with µ value at any time.
Figure 4 - 7: CDF of passengers arriving at airport for flight (i) for a given time before the flight under different
(µ): time of passenger arriving (a) Ω = 120 min; (b) Ω = 150 min; (c) Ω = 180 min; (d) Ω = 210 min; (e) Ω =
240 min
(a) (b) (c)
(d) (e)
96 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
4.4.2 Behaviours of arrival pattern
Figure 4-8 illustrates the arrival profile of the departing passengers for a given time
before the scheduled departure time under different values of mean µ. As shown in the figure,
for a given time before the scheduled departure time, the behaviour of arrival patterns are the
same for every mean value. Furthermore, for each plot the peak value is the same for any mean
values. All plots reach their peak in the time interval 05:00 h to 10:00 h, and in the time interval
20:00 h to 24:00 h. In this case, for any graphs Ω ≤ µ, arrival patterns show little variation
compared with what was seen in case study one. In addition, the number of passengers reaches
the possible maximum at the end of three hours’ time.
Figure 4 - 8: Departing passenger arrival profiles at airport terminal for different (µ) given (Ω): (a) Ω = 120
min; (b) Ω = 150 min; (c) Ω = 180 min; (d) Ω = 210 min; (e) Ω = 240 min
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 97
4.4.3 Results of the simulation and discussion
From the data collected from the five different simulation scenarios in outbound terminal
processes including check-in, security screening and immigration processes, it was found that
that queue length and waiting time increase as µ increases, especially for the security and
immigration domain (Table 4-4). According to graphs (a) to (f) in Figure 4-9, it is evident that
for any µ and Ω combination, the queue lengths increase from check-in to immigration
processing points. Furthermore, the overall behaviour of the three queues with different µ
values are dissimilar for each Ω values. However, in graphs (a) to (c), the maximum queue
length for immigration increases with µ, until µ = 120 min.
98 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Table 4-4: Detailed output of ExtendSim simulation model
Scenarios Check-in Security Immigration
Ω µ values Max
queue
Ave
queue
Max
waiting
Ave
waiting
Max
queue
Ave
queue
Max
waiting
Ave
waiting
Max
queue
Ave
queue
Max
waiting
Average
waiting
Ω = 120 min
60 46 2.06 23.58 3.38 111 7.68 12.45 1.87 413 61.48 82.33 20.37
90 22 0.81 10.67 1.33 210 18.71 21.86 4.57 431 72.58 86.15 23.8
120 37 1.12 14.82 1.86 163 13.44 16.9 3.29 443 80.74 88.57 26.61
Ω = 150 min
60 40 1.04 17.85 1.7 105 4.33 11.14 1.06 321 43.38 64.06 14.42
90 16 0.288 3.25 0.25 187 16.29 20.19 3.99 376 55.47 75.13 18.32
120 11 0.2 4.28 0.33 178 15.38 18.64 3.75 413 68.45 82.47 22.45
150 11 0.11 4.03 0.19 140 9.85 15.28 2.41 389 65.82 77.69 21.89
Ω = 180 min
60 40 1.14 15.29 1.9 126 7.07 13.04 1.73 344 47.66 68.66 15.91
90 14 0.31 6.16 0.51 138 9.35 15.11 2.27 353 54.84 70.51 18.34
120 7 0.08 3.92 0.124 181 13.34 19.86 3.25 408 70.36 81.69 23.19
150 12 0.11 4.68 0.18 137 10.29 15.16 2.51 407 72.59 81.33 24.16
180 8 0.04 3.24 0.07 163 11.31 18.19 2.77 359 58.21 71.68 19.63
Ω = 210 min
60 59 2.02 27.54 3.33 103 5.46 10.85 1.33 237 34.51 47.51 11.43
90 19 0.42 9.53 0.71 109 5.76 12 1.41 366 60.36 73.09 20.01
120 27 1.01 12.21 1.7 95 3.93 9.93 0.96 306 46.01 61.2 15.05
150 4 0.03 1.48 0.04 127 9.63 13.13 2.36 371 62.97 74.22 20.68
180 7 0.07 2.87 0.011 128 9.84 14.06 2.41 393 66.74 78.26 22.22
210 42 2.19 19.98 3.61 151 11.94 15.84 2.91 396 67.79 79.13 22.56
Ω = 240 min
60 84 2.95 37.61 4.87 54 1.76 5.36 0.43 220 20.85 43.93 6.86
90 34 0.56 14.03 0.92 49 2.88 5.26 0.71 295 47.22 58.88 15.58
120 16 0.12 6.21 0.2 125 8.3 13.28 2.03 330 49.12 65.77 16.33
150 8 0.07 3.14 0.11 116 6.13 12.2 1.51 379 66.76 75.96 21.87
180 31 0.7 14.2 1.16 121 7.59 13.06 1.86 382 64.3 76.22 21.21
210 69 3.21 27.59 5.27 78 3.56 8.94 0.087 294 46.93 58.49 15.61
240 92 6.38 42.42 10.52 207 19.25 22.75 4.7 393 62.09 78.59 20.66
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 99
Figure 4 - 9: Queue lengths of different mean value at a given time before flight
100 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Figure 4-9 (b) and (c) show a decrease of the above variable for any µ afterwards.
The corresponding µ value for check-in remains at 60 min until Ω increases up to 210
min. Afterwards, the corresponding maximum queue length occurs µ = 240 min for Ω
= 240 min. The minimum queue length for check-in starts to occur at 90 min at Ω of
120 min. As Ω increases, this value reaches 150 min. The variation of queue length
with changing µ value is not always the same for any time Ω values. Based on the
results of simulation, immigration counters face considerable congestion most of the
time.
It is observed that passengers who arrive ≥ 2.5 hours before the departure time
are more likely to be served with the minimum waiting time, especially at check-in
and security, except if Ω = µ. Another observation is that the maximum queue length
of check-in counters occurred in the case of µ = 60. Overall, the departure processes
are interdependent with each other and impact on each other, which means if the queue
length of the check-in domain is increased, the queue length at the other processes
decreases, hence, the proposed model can be a very useful approach to balance the
performance of terminal operations.
4.5 SELECTION OF BEST TIME TO ARRIVE AT THE AIRPORT BASED
ON THE NORMAL DISTRIBUTION
In the previous two studies, the impact of passenger arrival patterns on airport
terminal facilities was investigated. Two case studies were conducted, the first one
investigated the impact of different arrival times before departure flight having the
same mean values. The second case study examined the influence of different mean
values for a given time before the flight was simulated. In this section, the best policy
of passengers arriving at the airport based on the two basic parameters of time before
flight Ω and mean µ time before departure will be determined. The results of
simulation for any combination of Ω and µ are presented in the table 4-5 below.
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 101
Table 4 - 5: Summary of the simulation results
102 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
4.5.1 Selection of the best scenario at each process
Figure 4-10 shows queue length and waiting for the check-in processes
considering all scenarios of the time before the flight. It can be observed that scenarios
5 and 26 have the highest values of queue length and waiting time, which means
whenever passengers came at Ω > 180 min.
Figure 4 - 10: Check-in queue length and waiting time for all scenarios
Figure 4-11 illustrates the queue length and waiting time of the security
screening checkpoints. From the graph below, it can be seen that the maximum queue
length and waiting time occurred in scenarios 7 and 26. While the minimum queue
length and waiting time occurred in scenario 19, which means the security screening
will have the optimum performance when passengers arrive 3.5 hours beforehand, with
the mean value of 150 min.
Figure 4 - 11: Security screening queue length and waiting time for all scenarios
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 103
Figure 4-12 shows queue length and waiting time at the process of immigration.
It is clear to see that the best scenario that meets both metrics is scenario number 5. It
is suggested that passengers should come to the airport four hours prior to the departure
time, with the mean value of 60 min.
Figure 4 - 12: Immigration queue length and waiting time for all scenarios
4.5.2 Aggregation of all processes
In this section, we will introduce the strategy of selecting the best policy of
passenger arrivals at the airport. This strategy aims to find the optimum policy
regarding the minimum waiting time queue length at each processing point, including
check-in, security checkpoints, and immigration for all 26 simulated scenarios. These
scenarios are the combinations of the two main parameters of Ω and µ (refer to Figure
4-12).
i. Sets of time before departure time (Ω) = 120 min, 150 min,
180 min, 210 min, 240 min
ii. Sets of the mean value of time before flight (µ) =
60 min, 90 min, 120 min, 150 min, 180 min, 210 min, 240 min
The procedure of this strategy is that each processing point (check-in, security
and immigration) was selected with each time having highest priority. Seven different
scenarios have been conducted to provide accurate results that satisfy all possible
scenarios that might occur in the real world, as each airport behaves differently, and
its elements have unique functioning features (Manataki & Zografos, 2010).
104 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Given that check-in = a, security = b, immigration = c, the combinations of
possible conditions are introduced in the following sets:
𝑎 = 𝑏 = 𝑐, 𝑎 < 𝑏 < 𝑐, 𝑎 < 𝑐 < 𝑏, 𝑏 < 𝑎 < 𝑐, 𝑏 < 𝑐 < 𝑎, 𝑐 < 𝑏 < 𝑎, 𝑐 < 𝑎 < 𝑏
Figures 4-14(a) – (g) show the results of aggregation of the waiting time and
queue lengths for the three processing points (check-in, security and immigration) that
were selected in this study. Table 4-6 summaries the seven scenarios under different
priority for each process. Considering such scenarios would assist us in gaining
insights into the real-life scenarios since each airport operates differently and has
different behaviours with different priorities. Figure 4-13(a) demonstrates that all three
processes have the same priority with an equal factor of 33.3%. This factor is
multiplied by the KPIs values of each process. For each scenario, we then compute the
sum of the particular KPI value to reach the total value for all three processes. This
process is repeated for the remaining scenarios listed in Table 4-6.
Table 4- 6: Illustration of different conditions to select the best scenario
Scenario Condition Check-in factor
(%)
Security factor
(%)
Immigration
factor (%)
1 𝑎 = 𝑏 = 𝑐 33.3 33.3 33.3 2 𝑎 < 𝑏 < 𝑐 20 30 50 3 𝑎 < 𝑐 < 𝑏 20 50 30 4 𝑏 < 𝑎 < 𝑐 30 20 50 5 𝑏 < 𝑐 < 𝑎 50 20 30 6 𝑐 < 𝑏 < 𝑎 50 30 20 7 𝑐 < 𝑎 < 𝑏 30 50 20
(a)
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 105
(b)
(c)
(d)
106 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
Figure 4 - 13: (a-g) the impacts of different arrival patterns based on the priority for each processes
(e)
(f)
(g)
Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes 107
For the above scenarios, it is clear to see that the graphs in Figure 4-13 (b) – (d)
have the same behaviours and patterns when considering immigration is the higher
priority, especially for the Figure 4-13 (b) and (d) graphs. The minimum queue length
value occurred in scenario number 7 when passengers arrive at the airport two hours
beforehand under given mean value 90, yet the minimum waiting time can be seen in
both scenarios 7 and 13.
Figures 4-13 (e) - (g) have slightly different patterns when assumed that check-
in is the higher priority. It is observed that the check-in process has a significant impact
on the system when it is considered as the high priority, which leads to a smaller
number in the queue and shorter waiting time spent in the departure system. Thus, it
can be concluded that investigating arrival patterns of departing passengers is critical
and has relevance for planning, as this would assist airport management to respond
effectively and provide a better quality of service, enhancing passenger satisfaction.
Table 4-7 summarises the best policies that satisfied check-in, security and
immigration in terms of minimum queue length and waiting time.
Table 4-7: Summary of the results of selection of the best policy of time before flight
Case # Condition Best scenario considering
queue length
Best scenario
considering
waiting time
1 𝑎 = 𝑏 = 𝑐 7 13
2 𝑎 < 𝑏 < 𝑐 7 7
3 𝑎 < 𝑐 < 𝑏 7 7
4 𝑏 < 𝑎 < 𝑐 7 7
5 𝑏 < 𝑐 < 𝑎 7 13,16
6 𝑐 < 𝑏 < 𝑎 13 13
7 𝑐 < 𝑎 < 𝑏 7 13
4.6 CHAPTER SUMMARY
This chapter has analysed the influence of different arrival patterns on passenger
processing activities including check-in, security, and immigration using discrete
event simulation in order to address question two. These results provide important
insight into the two important parameters, namely time before flight Ω and mean µ
before the flight and how they impact the performance of an international terminal
108 Chapter 4: The Impacts of Arrival Patterns on Airport Mandatory Processes
system. Observations of the first experiment show that for a given µ under different Ω
values there is a significant influence on the departing passengers’ profiles, especially
during time before flight Ω ≤ µ. However, in the second set of experiments, the
behaviour of arrival patterns was similar for all scenarios. Furthermore, the simulation
results demonstrate how much congestion the airport will incur.
In case study one, it was observed that for a given µ the peak value for the
probability distribution increases when time before flight decreases. Furthermore,
having different times before flight under a given mean caused more of a significant
impact on departing passenger profile, especially when time before flight Ω ≤ µ. In the
terminal facilities, the queue length and waiting time decreased if the time before the
flight increased for any mean value. This impact was seen more in the security and
immigration processes. In contrast, for case study two, the behaviour of CDF and
arrival profile of departing passengers is similar for varying mean µ under for a given
time before flight. Moreover, the queue time and waiting were seen to increase from
check-in to immigration. In addition, the overall behaviour of the three queues with
different µ values are dissimilar for each Ω value.
The scenario of a passenger arriving at the airport terminal four hours beforehand
for a given mean µ value of 60 was found to be the best policy considering queue
length if the check-in and security were selected to be more important factors. In the
rare condition that all the processes are equal, scenario 7 and scenario 13 (passenger
arriving at the same time but under mean value µ 90) seem to be the best scenarios
when considering both queue length and waiting time.
In the next chapter, the simulation model will be extended to include inbound
processes and integrated with an Advanced Resource Management (ARM) approach
as the next phase of the overarching model
.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 109
Chapter 5: A Framework for Sharing Staff
between Outbound and Inbound
Airport Processes.
5.1 INTRODUCTION
In Chapter 3, the first phase of the overarching model was presented. It focussed
on the processes and sub-processes of the outbound system of an international airport
terminal, based on the discrete event theory discussed in sections 3.2 and 3.3. This
chapter will extend Chapters 3 and will introduce an integrated simulation model of
an airport terminal involving both inbound processing points and an Advanced
Resource Management (ARM) model. The model describes the potential interactions
between inbound passengers and outbound passengers, resulting in competing
priorities regarding physical space. This competition results in the need to model and
optimise these interactions under realistic terminal conditions.
It is believed that the development of such a model, including all airport aspects
(i.e. outbound and inbound), has become very important since most of the current
research is predominantly and separately focussed on airport operations associated
with the departure (outbound) passenger or the arrival (inbound) passenger. In
addition, this model provides a clear idea of passenger flows through the entire airport
terminal processing procedures. The objective of developing such a model is to
examine the possible bottlenecks in arriving and departing passenger flow. It also
provides a platform for studying more complex processing behaviour and operational
strategies using the simulation environment, ExtendSim. Consequently, insight into
current and future situations of airport systems will be gained. Section 5.2 discusses
the generic framework for inbound process flow of Australian international airports.
Section 5.3 illustrates the ExtendSim simulation models for terminal arrival processes.
Section 5.4 describes the development of resource allocation management algorithms,
including integrated and non-integrated modules.
110 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
5.2 INBOUND PASSENGER FLOWS MODELLING
5.2.1 Outline of inbound flow processes
This section discusses inbound passenger flows at Australian airports. Wu,
Pitchforth, and Mengersen (2014) identified the inbound facilitation procedures
through consultations with airport specialists, including the airport operator,
biosecurity, border protection and customs. Based on Wu et al. (2014), incoming
passenger facilitation processes can be divided into four systems and sub-systems,
including arrival concourse, entry control point, baggage hall and secondary
examination area, as shown in Figure 5-1.
Figure 5 - 1: An illustration of inbound passenger facilitation processes (Wu et al., 2014).
As can be seen from Figure 5-1, these elements consist of both types of
activities: mandatory and discretionary (Wu et al., 2014). Mandatory activities refer
to activities that each passenger must pass through, such as immigration, security
screening, and quarantine; while discretionary activities refer to the optional activities,
such as duty free shopping and restroom usage.
At each process of the inbound system, passengers have different levels of
interactions. For example, at the immigration domain interface, a passenger interacts
with the personnel, equipment, and process, while at the disembarking area, a
passenger interacts with the airline service, personnel, and process (Popovic, Kraal, &
Kirk, 2010). In addition, there are many factors that influence processing time and
processing policies of inbound passengers at different processing points. An example
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 111
of these factors is the nationality of the passenger, which can have a great impact on
processing time at immigration checkpoints for two reasons. The first reason is that
passengers from countries like Australia, New Zealand and the USA, can use the new
SmartGate technology at immigration, while other passengers are processed manually
through immigration counters. The second reason is that some counters have stricter
visa requirements which might take extra time, for instance, for customs agents to
process (Pitchforth, Wu, Fookes, & Mengersen, 2015). The purpose of this research is
to determine the major components that are able to define the inbound passenger flow
within an international airport terminal. These components consist of disembarking,
immigration control point, baggage claim, and secondary examination area operated
by quarantine. The other points of discretionary activities, such as restrooms and
restaurants, are omitted from this study.
5.2.2 Inbound process simulation modelling
The process of the simulation model of the inbound system is illustrated in
Figure 5-2. The arriving passenger will disembark from the aircraft when it arrives at
the assigned gate according to flight schedules and other related factors, such as
aircraft size. For the passengers who have a connecting flight, they will be directed to
the security screening checkpoints, and then they will proceed to the gate for another
flight. In this research, it is assumed that transit passengers are within the concourse
only. The main input data of the inbound simulation model is introduced. The input
model consists of three input data points, including flight attributes, inbound passenger
attributes, and the requirement of passenger processing points.
Figure 5-3 demonstrates the modelling of relevant input data of the inbound
simulation system. In the first part of the graph (left-hand side), the inbound flight
attributes data are introduced. As we mentioned earlier in section 3.5, flight attributes
are considered the vital elements of simulation input data. They consist of three types
of data: flight schedule, number of passengers, and passenger’s characteristics. The
inbound flight schedule contains relevant flight information, i.e. scheduled time of
landing, gate number, flight code, and flight airline. The second type of data that are
required to estimate the number of passengers per flight are the size of the flight and
load factor. The load factor varies based on the type of airline and the day of the week
(Manataki & Zografos, 2009b). The last type of input data are the characteristics of
112 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
the passengers. Some assumptions have been made in relation to passenger
characteristics.
Figure 5 - 2: Flowchart of the upper level of the inbound process flow model
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 113
Figure 5 - 3: The input modelling of the inbound simulation model
5.3 INBOUND EXTENDSIM MODULE DESCRIPTION
In this section, a discrete event simulation was utilised to simulate inbound
process flow using ExtendSim software. The inbound simulation models can be
organised into two hierarchical levels. The first level of the hierarchy reflects the
inbound airport system broken down into a set of the main inbound procedures. The
second level describes the intricate details of the different sub-processes in the airport
114 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
terminal. Specifically, the main inbound procedures that the airport terminal model
consists of are:
Passengers disembarking, including generating inbound passengers’
attributes, such as SmartGate users, walking speed, number of bags etc.;
Inbound security screening checkpoint for inbound passengers, assuming
15% of total transfer passengers failed in the first attempt. This module
takes into consideration x-ray checks and secondary screen checks (i.e.
random checks);
Inbound immigration processing points, including SmartGate services and
the manned counter for passport control;
Quarantine processing checking points, including two separate queues to
process arrival passengers: one for passengers who have something to
declare, and another for passengers who have nothing to declare.
The simulation contains seven blocks, which demonstrate the actual practised
process in the inbound system of Australian airports. The first block describes the main
processes of the arriving system around the basis of a hierarchical module design, such
as disembarking passengers, security screening for transit passengers, inbound
immigration, waiting time in the baggage claim, waiting time at duty free, and
quarantine. Each one of these blocks contains many pre-programmed blocks of the
software with different functionalities: blocks setting attributes of passengers, or
setting parameters of processes, programmes or generators; blocks to display
information about the processes; blocks to perform a service, or queues; and other
blocks to perform the mathematics of logical operation (Olaru & Emery, 2007). The
second block discusses the process of creating entities in the model that refer to arrival
passengers – this will be explained later in section 5.3.2. Arrival flights have two types
of passengers: transit and non-transit. Transit passengers refer to those who have a
connecting flight, while non-transit refers to passengers who enter the final destination
of their journey. For transit passengers, block three was developed to simulate the
security screening process, as an interface point between the inbound and outbound
systems to link transit passengers with departing passengers at boarding gates.
The rest of the passengers will be routed to pass through the duty free area, as
illustrated in block four. Passengers reach the first processing point of the inbound
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 115
system of immigration for passport checks, as shown in block five. After this step,
passengers go to the baggage claim area to collect their bags, which is presented in
block six. Block seven illustrates the quarantine system, including both the lines of
something to declare and nothing to declare, using the hierarchy block. The second
level of the block simulates quarantine stations to scan and check if there are any
explosive or illegal items.
5.3.1 Block 1: Hierarchy blocks for inbound processes
In order to develop a robust inbound simulation model, the major elements that
represent the system should be extracted. The elements of the inbound system are
disembarkation passenger, security screening for transit passengers, time spent at
duty-free, inbound immigration, baggage collection and quarantine. Figure 5-4 shows
the upper level of the arrival passenger flow model at an Australian international
airport. Hierarchy blocks of ExtendSim were used to easily build a model with several
levels, where each reflects one particular process of passenger handling and its sub-
processes. Each hierarchy block has a unique functionality, hence the investigation of
the particular process, as an observation needs to be made on just one level. The
enumerated hierarchy block above will be explained further in the following sections.
Figure 5 - 4: Block 1: The high level of inbound flow modelling
5.3.2 Block 2: Creating inbound passengers’ entities of an arrived flight
Figure 5-5 illustrates the structure of the block two hierarchy, ‘passengers
disembarking’. The block focusses on the process of creating passenger entities based
on arrival flight scheduling data. This block consists of four modules, each with unique
attributes to identify and route the passenger throughout the map of the model.
116 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 5: Block 2: Structure of the hierarchical block ‘passengers disembarking’
There are three steps to generate inbound passengers. The first step is that flight
scheduling data were used to set up a flight attributes table, considering information
related to arrival flights, such as flight airlines, arrival time, assigned gate, and flight
capacity (Figure 5-6). The number of passengers and how this relates to other
variables, such as flight size and load factor, need to be examined. The methods used
to determine the number of passengers have been previously examined (Manataki &
Zografos, 2009b; Schultz & Fricke, 2011).
Figure 5 - 6: Inbound flight attributes
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 117
The next step is generating inbound passengers’ attributes. This can be done
through the development of an algorithm using an Excel macro visual basic
programme. Figure 5-7 illustrates the flowchart of the algorithm that was used for this
purpose. Unlike outbound passengers’ arrival time patterns, inbound passengers
reached the terminal at one time rather than arriving three hours beforehand.
Figure 5 - 7: Algorithm for creating inbound passenger attributes
Figure 5-8 shows a snapshot of inbound passenger attributes which will be the
input for the ExtendSim simulation model. Each row represents the major information
related to an arrived passenger, including arrival time, flight number, gate number,
number of bags, and walking speed. Based on the given attributes, ExtendSim can read
particular attributes at any processing station and deal with it. For example, since there
are two types of passengers in the immigration system, a decision module is used to
identify the type, and guide them to the proper processing point.
118 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 8: Inbound passenger attributes
The third step is to link the inbound passenger table with ExtendSim through a
global array database, as explained in Chapter three, section 3.5. The global array can
be defined as a two-dimensional (row and column) array of data that is reachable by
any block in the model. The role of a global array is to share information between
blocks when a direct connection is either impossible or inconvenient, to exchange data,
or to store information that can be accessed by a row and column index. Figure 5-9
clarifies the mechanism for connecting the data source developed in the Excel
worksheet with ‘block two’s’ disembarking passenger.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 119
Figure 5 - 9: Block 2: Mechanism of linking inbound passenger attributes with the ExtendSim model.
In addition, block two considers the variable of a passenger’s walking time
between the gate and the first processing point. Walking time for each passenger can
be calculated by dividing walking speed by the distance between a particular gate and
the immigration processing point. It is assumed the distribution of walking speed is
120 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
three to five kilometres per hour depending on the age of the traveller, according to
TranSafety (1997) study. The following figures explain the technique that ExtendSim
uses to calculate walking time. First, Get-Module is used to determine the distribution
of walking speed of specific passengers, as illustrated in Figure 5-10.
Figure 5 - 10: Block 2: Walking speed module
The second step is determining the gate number and the distance between the
gate and the first processing point. As it can be seen in Figure 5-11, Get-Module is
used to read the gate number of each arriving passenger, which is linked to the lookup
table module. The reason for using the lookup is to store the data of the gate number
and the distance between each gate to the first processing point.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 121
Figure 5 - 11: Block 2: Arrival calculating gate distance module
Lastly, an equation module is connected to calculate the value of walking time
and to send this value to the activity module to be processed, as shown in Figure 5-12.
Figure 5 - 12: Block 3: Walking time module
122 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
5.3.3 Block 3: Inbound security module
The module of inbound security is developed to process passengers who have a
connecting flight. This module is built based on three levels. The first level
demonstrates the hierarchy block of the x-ray system and the decision module for the
random check queue, as shown in Figure 5-13. According to Maertens and Grimme
(2015), the assumption is made that only 20% of total inbound passengers are
considered transit passengers.
Figure 5 - 13: Block 3: The hierarchy module of x-ray and routing for random check
The second level represents the x-ray system which is further split into two sub-
modules, including the queueing system and workstations, as illustrated in Figure 5-
14. At the inbound security point, the passenger is processed based on the FIFO
method which serves each passenger at a time, but the passenger with the shortest time
will be processed first.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 123
Figure 5 - 14: Block 3: Simulated queue of x-ray check and hierarchy block of workstation
The third level demonstrates the activity block for processing items
(passengers/ongoing bags). The characteristics of processing the objects can be
defined as the distribution of processing items and the capacity of workstations to
process each time unit, as shown in Figure 5-15. As can be seen, the maximum item
in the workstation is one each time, the processing time distribution is from ~Tri (0.2,
0.5, 0.75) where the min is 0.2, max is 0.75, and the most likely value is 0.5.
124 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 15: Block 3: Characteristics of processing items
As previously explained, the write and read modules were used to read data from
or write data to the ExtendSim database, such as global array or Excel workbooks.
Thus, write modules are linked to queue modules to record each minute of the output
of queue length and waiting time at the inbound security system; this is illustrated in
Figure 5-16.
Figure 5 - 16: Block 3: Storing the outputs of security
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 125
After that, the decision module is used to assign passengers that have been
chosen for a secondary security check (at random), as shown in Figure 5-17.
Moreover, Figure 5-17 shows the processing time distribution of passengers selected
for the random explosive trace inspection.
Figure 5 - 17: Block 3: Random explosive decision module
Finally, the queue module is used, followed by the activity module to simulate
the random explosive process. From the graph below, it can be seen the passenger
processes based on the FIFO method, while the distribution of processing time is from
~Erlang (0.1, 0.2), as shown in Figure 5-18.
126 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 18: Block 3: queuing and processing time characteristics of random explosive check
5.3.4 Block 4: Duty free
After a decision module is used to distribute entities with 20% for transit
passengers, the rest will be routed to the inbound immigration system through duty
free shops. Thus, this block simulates passengers who wish to shop in the airport duty
free. It is assumed that 40% of total inbound passengers will spend some time at duty
free, with the assumption of uniform distribution (1, 24) minutes (Philip J Kirk,
2013a). Get-Module is used to identify the time that each passenger spends shopping
in duty free, as shown in Figure 5-19.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 127
Figure 5 - 19: Block 4: Assign duty free attributes module
5.3.5 Block 5: Inbound immigration and customs module
The system of immigration consists of two subsystems, including common
immigration counters and SmartGate (Wu et al., 2014). Similar to the processes of
immigration in outbound passengers, arrival passengers must join the allocated queues
formed in front of immigration booths, including Australian and international
passengers, to complete the processes required to enter the baggage area. There are
two different queues in the immigration domain because of the nationality of the
passenger. International passengers, who are not allowed to use SmartGate, unlike
Australian, Swedish and New Zealand citizens, have special queues, which are not
usually crowded with an assumption of 30% of total passengers. In immigration
domain, passengers should present their passport and incoming card, and should get
their visa checked by immigration staff to assess its validity. It is noted here that the
immigration area is considered the first bottleneck in the arrival terminal because of
the length of the queue there (QUT, 2010). The major elements of the inbound
immigration processes are illustrated in Figure 5-20.
128 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 20: Flow chart of inbound immigration checkpoint process
Figure 5-21 demonstrates the logic design of the inbound immigration system.
The logic can be divided into four sections; each section containing different
functionalities and modules. These modules are the decision module, queue module,
common workstation module and SmartGate module.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 129
Figure 5 - 21: Block 5: Logic design of inbound immigration checkpoint process
At the beginning of simulating immigration counters, a decision module was
used to identify the attributes of SmartGate users and direct them to two ends which
both connected to a queue module, as illustrated in Figure 5-22.
Figure 5 - 22: Block 5: Inbound SmartGate user decision module
130 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Another module in block five is common immigration queue characteristics.
International passengers who are not eligible to use SmartGate services will be held in
front of common immigration counters and served based on the FIFS queuing method,
as showed in Figure 5-23. The queue module is linked to read and write modules to
record the results of each minute during the simulation.
Figure 5 - 23: Block 5: Inbound immigration queue module
Then, the activity module is assigned to configure immigration workstations
which consider the characteristics of processing passengers. As can be seen in Figure
5-24, the distribution of processing at each workstation followed a triangular
distribution where the maximum was 1.5, the minimum was 0.5, and the most likely
value was one minute. It is assumed that each counter can process one passenger at a
time. Also, write and read modules are used to record the output of queue length and
waiting time.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 131
Figure 5 - 24: Block 5: Process characteristics of inbound immigration
The last module of block five is the logic design of SmartGate processing points.
Passengers can be serviced based on the availability of self-process kiosks. The
proposed module is flexible because of the ease of adding/removing SmartGate kiosks
or the parameters of processing time distribution; this is illustrated in Figure 5-25.
Figure 5 - 25: Block 5: Logic design of the SmartGate module
132 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
5.3.6 Block 6: Baggage claim module
As passengers exit the system of immigration entry point, they are directed to
the right carousel to collect their baggage (QUT, 2010). Also, passengers may search
for trolleys, which can be found in the luggage hall. The passengers can also get
information about the appropriate carousel by looking at the screens located in the
luggage hall or by inquiring with officers. Figure 5-26 demonstrates the decision
module to identify passengers who have bags that need to be claimed.
Figure 5 - 26: Block 6: Baggage claim decision queue module
Passengers can perform two different discretionary activities in the baggage hall,
including the disposal of quarantined items in designated bins and the use of
restrooms. It is assumed that the distribution of the number of bags = uniform (0, 2),
where the delay time is assumed to be the normal distribution for a given mean value
(µ) of 10 minutes and standard deviation value of three minutes, as shown in Figure
5-27 (Kirk, 2013; Ma, 2013).
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 133
Figure 5 - 27: Block 6 baggage claim delay time module
5.3.7 Block 7: Inbound quarantine module
The last stage of the inbound facilitation process is quarantine. In this area, the
incoming passenger card should be presented by all passengers to the customs marshal.
Based on the items written on the incoming card, the customs marshal decides whether
the passenger exits the airport or needs to go to a quarantine or customs check.
Passengers are subjected to a Care Quality Commission (CQC) check if they have
declared one or more of the following:
Food items;
Wooden objects, plants, herbs, seeds, or traditional medicines;
Animals, animal parts, or animal products;
Soil, or articles that been in contact with soil;
Have visited one of the rural areas, been in contact with animals, or been in
the farms outside Australia in the past 30 days;
Have visited Africa or South America in the previous six days.
Passengers who need to go to a quarantine check will join the special queue for
that. Here, the CQC officer calls each passenger and asks them to load their luggage
134 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
onto the x-ray machine to scan. The CQC officer will see if the luggage needs to be
opened to do more checking after passing it through the x-ray machine; then, they will
assess the items and decide if these items are permitted or prohibited. If these items
are permitted, the passenger can exit this area; however, if the items are prohibited,
they will be confiscated and the passengers will either be directed to the exit or held
for further interrogation. Figure 5-28 shows the quarantine procedures.
Figure 5 - 28: Flow chart for quarantine process
On the other hand, if passengers have declared one or more of the following,
they will be subjected to a customs check:
AUD $10,000 or more in Australian or foreign currency equivalent;
Duty- and/or tax-free items worth AUD $900 or more purchased or obtained
overseas;
Declaration of prohibited or restricted items, such as medicines, steroids,
firearms, weapons, or any kind of illicit drugs;
Possession of goods/samples for business or commercial use;
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 135
Possession of more than 2250 ml of alcohol or 250 cigarettes or 250 g of
tobacco products.
When the customs officer calls the passenger to proceed to the customs counter,
passengers will be asked to put the luggage in the x-ray machine to be checked and
scanned. After that, the customs officer will check whether the luggage needs to be
opened or not and if it needs to be opened, the officer will carry out the search. The
passengers declaring AUD $10,000 or more in Australian or foreign currency
equivalent will be asked to fill in the ‘money movement form’. Those who have
obtained duty- and tax-free items worth more than $900, or more than 2250 ml of
alcohol, 250 cigarettes, or 250 g of tobacco products, will be asked to pay customs
fees or have some of the items confiscated by the customs officer. Passengers deciding
to pay the customs fees will proceed to the cashier for the payment, and then continue
to the exit. In case of a passenger’s refusal to pay the customs fees, the customs officer
will detain the items according to policy and then direct the passengers to the exit
(QUT, 2010). It is noted here that the second bottleneck is the waiting time to undergo
customs/quarantine checks (QUT, 2010).
The quarantine system is built based on two identical sections, where each
section reflects the process and sub-processes of the system. Figure 5-29 shows the
logic chart of block seven, the system including the routing decision module.
Figure 5 - 29: Block 6: Logic chart of quarantine module
136 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
To begin with, a decision module is used to determine an attribute of travellers
who need to declare what they bring into Australia. Then the passengers will be split
up depending on their given attributes and directed to the proper line to be get
processed, as shown in Figure 5-30.
Figure 5 - 30: Block 7: Inbound declaration decision module
Each sub-system has its characteristics of holding and processing passengers.
Thus, two separate lanes are created linked with activity modules. Figure 5-31
illustrates the queue module of the declaration lane where passengers are processed
based on the FIFO queueing method. The write and read module is linked with the
queue module to record the output data, such as waiting time and queue length at
processing points.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 137
Figure 5 - 31: Block 7: Declaration queue module
Passengers have been classified into two types during the declaration process.
The first type is the passenger who brings permitted items to the country and
announces them on the incoming card. The activity modules are used to simulate the
declaration process, where the delay time is assumed to follow uniform distribution
between one to five minutes, as shown in Figure 5-32.
(a)
(b)
138 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 32: Block 7: Inbound immigration queue module
Another type is the passenger who breaks the law by bringing illegal goods into
the country without announcing them. The decision module is used to assign such
passengers and direct them to the next processing point for further inspection. Figure
5-33 shows the secondary inspection process.
Figure 5 - 33: Block 7: Inbound Quarantine queue module
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 139
On the other side, there are some other passengers who have nothing to declare.
However, they still need to have their baggage scanned through the x-ray machine.
Therefore, the same model has been configured with the assumption that passengers
are processed depending on the sort of FIFO queue method, as illustrated in Figure 5-
34.
Figure 5 - 34: Block 7: Nothing to declare queue module
Figure 5-35 illustrates the process characteristics in the nothing to declare lane
system. It is clear to see that the maximum items in activities is 1 each time.
140 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 35: Block 7: Quarantine workstation for nothing to declare line
5.4 INTEGRATED INBOUND AND OUTBOUND PROCESSES
At this stage, the outbound and inbound simulation models can be run
simultaneously. This can be done by simulating the major processes of both sides and
developing passenger attribute modules using flight schedules of arrival and departure
flights, as explained previously in Chapter 3 (section 3.5) and section 5.2.2. The model
can give a broad picture of the status of entire terminal operations and how they impact
each other. It can also be very supportive in identifying any bottlenecks in the system.
However, because of the randomness of the events and that each system works in
isolation, further improvement can only occur by development of the ARM approach.
The development procedures for such a model are discussed as follows.
5.4.1 Advanced resource management (ARM)
This section discusses the development of an advanced resource management
model. Figure 5-36 shows the overview of passenger flow at the international terminal
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 141
building, as well as departure processes from check-in to boarding, and arrival
processes from de-boarding to baggage claim and exiting the airport. Many issues
mean that managing airport staff can be difficult (Wu & Mengersen, 2013). One of
these issues is that each process is operated by a different stakeholder, for instance,
check-in and boarding is performed by the related airlines and the ground handlers
(Philip J Kirk, 2013b; Manataki & Zografos, 2009a). Another important issue of
managing terminal staff is that each airport domain behaves differently regarding
skills required to perform particular workstations. Chuin Lau (1996, p. 93) argued that
‘manpower scheduling is concerned with the scheduling of manpower resources to
meet temporal operational requirements in ways that satisfy the goals and policies
imposed by the management, labour union and the government’. Also, each process
has its own rules for allocating staff and ways for sharing the staff resources between
related areas. Given the complexity of an international terminal system, algorithms
have been developed to manage staff dynamically across the international terminal
(Ip, Chung, & Ho, 2010).
Algorithms can be divided into two different levels:
Algorithms for managing staff that are working on one system
(outbound/inbound) and can be shared with another process in the same
system, such as airline staff responsible for operating check-in, and could
share with boarding and quarantine;
Algorithms to control staff that work on a particular process that is located
on both sides (outbound/inbound), such as immigration and security (see
Figure 5-36).
142 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 36: Flowchart framework for ARM model
5.4.2 Mechanism of development of algorithms
The algorithm can be developed as follows:
1. Develop the simulation models, including the physical structure of
outbound and inbound processing checkpoints, as explained in sections
3.5.2. and 5.2.3.
2. Create a global database (see Figure 5-37) for each processing point for the
following reasons:
To store the attributes in a pair of dynamic arrays, one-dimensional to store
the name of each attribute, the two dimensional to store the value of each
attribute name;
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 143
To link the parameter table, including number counters, staff ID, the start
shift, and the end shift with ExtendSim.
3. Upload input data, represented by (1) staff attributes, (2) given rules
‘threshold of the queue of particular processes’, and (3) allocation methods.
These input data will be explained in more details in the next sections.
4. Provide a dynamic link between the dialog item (data table or parameter)
and the data source of a global array, as shown in Figure 5-38. Two blocks
named read and write are used to import and export information between
data source and the ExtendSim simulation model and vice versa. The
function of the read block is to read data from a data source to be used in a
model, while the function of the write block is to write data from an
ExtendSim model to a data source ‘global array’.
Figure 5 - 37: Resource allocation dialog for global array
144 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 38: Dynamic link between parameter tables and ExtendSim
5.4.3 The logic of development algorithms
The proposed approach behaves as dynamic management where the staff can be
controlled and allocated based on a given rule, such as queue length threshold. The
logic that has been followed to develop such algorithms is categorised into three
options. The first option is adding staff to a particular workstation. That can be done
by looping through across the queues of processing points and reading the status of
the queue; if the queue length exceeds the threshold, check if there is available staff in
the resource pool and add them to the first available spot. The second option is
removing the staff from the processing point and shutting down the workstation. This
can be done if the queue length is less than a given threshold.
The last option is more complicated because the staff need to be shared between
related processes. This option is considered after the loop through the staff resource
pool ‘global array’ databases is done and no available staff are found. Then, we need
to share resources between areas based on the queue threshold for each process; for
example, airline staff will be shared between economy and business counters with
more priority to the business class queue. The purpose of developing such algorithms
is to operate airport terminal processes at the optimum level.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 145
5.4.4 Input data of integrated model
1. Staff attributes
Since each process has different characteristics, staff attributes can be classified
into general and specific. As seen in Figure 5-39, there are four types of staff to operate
airport operational elements in this model. The first type is airlines that are responsible
for operating check-in and boarding domains of the outbound system (Philip J Kirk,
2013b). The second type is security which is operated by the airport owner. In
Australia, security is contracted out to expert companies to perform this terminal
domain. The third type is immigration and custom staff who are responsible for
controlling Australian customs and passport checks; this is the responsibility of an
Australian government agency. The last type is quarantine staff for border protection,
which are operated by distinct stakeholders of a government agency.
Figure 5 - 39: Staff attributes for the ARM model
2. Rules given for managing staff
The proposed approach allocates staff based on two different techniques. The
first type is a schedule-based method. In this method, the staff will be allocated and
used all the time to observe what the results are in terms of queue lengths and waiting
time. The second type is demand-based allocation. Unlike the first type, demand based
methods allocate and reallocate staff based on queue lengths rules. Additionally, in
this method, the staff will not only be shared between two processes, but also with the
Staff
Attributes
Type of staff (Airline, Security, Immigration,
Quarantine)
Shift time (start shift, end shift)
staff ID ActivityAttributes flightNum
AssignedStatus
Availability
•0= unavailable•1= available
•2= available, assign first
146 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
upcoming passengers to the particular processing point every 30 minutes. Regarding
the rules of queue threshold, action will be taken if queue length reaches X value, as
explained above.
5.4.5 Categories of algorithms
In this section, we will provide further explanation of the development
algorithms, including the aims and objectives, the functionalities, and the conditions
of the algorithms. The algorithms were developed to meet the behaviour of airport
terminal operations that occur in real life and have been classified into two categories
as explained below.
Algorithms developed for non-integrated processes
This section discusses the algorithms that were developed for the airport
terminal elements that are located on only one side, without integration between
outbound and inbound processes. An example of these elements is check-in/boarding
and quarantine. Figure 5-40 illustrates the flowchart of the airline staff dynamic model.
Airline staff is responsible for operating check-in and boarding domains. Also, they
allow passengers to drop their baggage and submit their passports and tickets to be
processed. The operating procedures of airline staff dynamic allocating is based on the
following algorithmic steps:
Step 1: Loop through the economy and business queues and read the queue
status.
Step 2: Check if the queue length exceeds the queue threshold and if there is
available staff in the resource pool, assign them to the first spot. If not, and the
queue length is less than the minimum limit, then remove the staff and shutdown
the workstation.
Step 3: (When step 1 and step 2 are not applicable), share staff between economy
and immigration counters, if there is no available staff in the resource pool. This
procedure is done based on the given policy and always prioritises business
class. The mechanism of moving staff is that if the business queue length
exceeds the queue threshold, such as five PAX, and there is more than one staff
member at economy counters, then move the staff from economy to business
counters and vice versa.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 147
𝑄𝐵 : Queue length at business check-in counters
𝑄𝐸 : Queue length at economy check-in counters
𝑇ℎ𝑄𝐵 : Threshold for business queue
𝑇ℎ𝑄𝐸 : Threshold for economy queue
𝑁𝐴𝑆 : Number of airline staff available in the database of global array
𝐴𝑆𝐶 : Airline staff for check-in counters assigned 𝑗 = 1,2,3 …
𝐴𝑆𝐵 : Airline staff for business counters
𝐴𝑆𝐸 : Airline staff for economy counters
𝐴𝑏𝑔𝑎𝑡𝑒 : Airline staff for boarding gate
G : Departure gate
𝐴𝑆𝑠ℎ𝑖𝑓𝑡__𝑠𝑡𝑎𝑟𝑡 : Start shift of airline staff
𝐴𝑆𝑠ℎ𝑖𝑓𝑡__𝑒𝑛𝑑 : End shift of airline staff
Figure 5 - 40: Flowchart algorithm for airline staff allocation module
148 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
The airline staff is also the operators of boarding. Thus, the integrated module
to share staff between check-in and boarding is developed. Before developing the code
of this algorithm, a global array database was created for boarding procedures and to
define and declare the related variables, such as flight code, boarding time, boarding
strategy, and gate number. As we can see in Figure 5-41, the first step of the algorithm
is to check the gate status by looping through all gates and seeing which one is active
based on the departure time. The second step is that if the gate is active, and there is
available staff in the global array database, assign the staff who are not at the end of
the shift.
Figure 5 - 41: Flowchart for integrated module for boarding procedure
Quarantine module
Quarantine is another domain that is located only in the inbound area. In this
domain, staff is shared between declare and nothing to declare lanes. The developed
module supports the two options of the resource allocation technique, including
schedule-based and demand-based methods. Figure 5-42 shows the flowchart
algorithms of quarantine staff management. The procedures for the resource
management approach of quarantine processing can be done via the following steps.
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 149
First step is to loop through the quarantine database and check if staff member 𝑖 is
available and not finished his/her shift. Second, every minute of simulation, we check
the demand column of the declare and non-declare processes and see if the queue
exceeds the threshold and staff need to be added or removed. If step one and two are
not applicable, we need to swap staff between both lanes based on the policy provided.
Lastly, for every minute, the code reads quarantine queues statuses and, for example,
if the queue length of the declaration lane exceeds the maximum limit, staff is moved
from nothing to declare to the declaration lane, and vice versa. The proposed
simulation allows for the determination of the following quantities, where:
𝑄𝐷𝑒𝑐 : Queue length to declare at quarantine processing point
𝑄𝑁𝑜_𝐷𝑒𝑐 : Queue length at nothing to declare queue at quarantine
𝑇ℎ𝐷𝑒𝑐 : Threshold of declaration queue at quarantine
𝑇ℎ𝑁𝑜_𝐷𝑒𝑐 : Threshold for nothing to declare queue at quarantine
𝑁𝑅_𝑠𝑡𝑎𝑓𝑓 : Number of quarantine staff available in the database of global
array
𝑅𝑅_𝑆𝑡𝑎𝑓𝑓 : Quarantine staff assigned 𝑗 = 1,2,3 …
𝑅𝑠𝑡𝑎𝑓𝑓_𝐷𝑒𝑐 : Quarantine staff for declare lane
𝑅𝑠𝑡𝑎𝑓𝑓_𝑁𝑜_𝐷𝑒𝑐: Quarantine staff for nothing to declare lane
𝑅𝑠ℎ𝑖𝑓𝑡__𝑠𝑡𝑎𝑟𝑡 : Start shift of quarantine staff
𝑅𝑠ℎ𝑖𝑓𝑡__𝑒𝑛𝑑 : End shift of quarantine staff
150 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 42: Flowchart algorithm of quarantine staff management module
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 151
Algorithms for integrated processes
Security staff management module
This section discusses the integrated model that is used to manage staff working in the
processes that are located on both sides of an international terminal. An example of these
processes is immigration and security screening. As explained earlier, security is operated by
the owner of the airport, but in Australia, security is contracted to third-party companies to
process passengers. The developed algorithm considers a VIP lane in the security system to
process diplomatic passengers. Moreover, the disembarking passengers from arrival flights
who have connecting flights have to pass through inbound security before reaching their
outbound boarding gate. Thus, the security staff need to be shared, not only between two lines
in the outbound system, but also with inbound security to process transit passengers. The
algorithm of the security sharing resource is presented in Figure 5-43.
𝑄𝑑𝑖𝑝 : Queue length at diplomatic security screening checkpoint
𝑄𝑛𝑜𝑛_𝑑𝑖𝑝 : Queue length at non-diplomatic security screening checkpoint
𝑄𝑖𝑛_𝑆𝑒𝑐𝑢𝑟𝑖𝑡𝑦 Queue length for inbound security
𝑇ℎ𝑑𝑖𝑝 : Threshold for diplomatic security screening checkpoint
𝑇ℎ𝑛𝑜𝑛_𝑑𝑖𝑝 : Threshold for non-diplomatic security screening checkpoint
𝑇ℎ 𝑖𝑛_𝑆𝑒𝑐𝑢𝑟𝑖𝑡𝑦 Threshold for inbound security screening checkpoint
𝑁𝑆_𝑆𝑡𝑎𝑓𝑓 : Number of security screening staff available in the database of global array
𝑆𝑆_𝑆𝑡𝑎𝑓𝑓 : Security staff assigned 𝑗 = 1,2,3 …
𝑆𝑆_𝑑𝑖𝑝 : Security staff for diplomatic lane
𝑆𝑆_𝑛𝑜𝑛_𝑑𝑖𝑝: Security staff for non-diplomatic lane
𝑆𝑆_𝑖𝑛𝑏𝑜𝑢𝑛𝑑 : Security staff for inbound lane
𝑆𝑆𝑠ℎ𝑖𝑓𝑡__𝑠𝑡𝑎𝑟𝑡 : Start shift of security staff
𝑆𝑆𝑠ℎ𝑖𝑓𝑡__𝑒𝑛𝑑 : End shift of Security staff
152 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Figure 5 - 43: Flowchart 1-2 of security resource allocation management
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 153
For the demand-based allocation method, the procedure of moving and managing
security staff is summarised based on the following algorithmic steps:
i. Create a global array database to declare variables related to staff attributes, such as,
start shift, end shift, staff availability. Also, global array is used to exchange interface
data with the internal data structure needed for ModL programming.
ii. Read the queue statuses of both sides, inbound and outbound, every one minute of a
simulation run and record which queue status is exceeded.
iii. Determine the available staff members who are not in the end of their shift and assign
them to the area where the queue has reached its maximum limit, and remove staff
if the queue is less than the minimum limit. If steps one and two are not applicable,
move to step four.
iv. Share the staff based on the given rules and priority. According to Odoni and de
Neufville (1992), the departure process, which sometimes involves services
provided to transit passengers, typically requires a significantly longer time than the
arrival process. The queue length of outbound security will be observed; if the queue
exceeds the maximum then move staff from inbound to outbound security screening
processing points, and vice versa, as shown in Figure 5-44.
Figure 5 - 44: Flowchart 2-2 of security resource allocation management
154 Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes.
Immigration inbound and outbound module
Figure 5-45 illustrates the flowchart of immigration resource management. The same
logic is also proposed for immigration resource allocation. As previously explained, priority is
always given to the outbound system because the time of departure is scheduled, which means
that passengers might miss their flight if they do not arrive on time. The procedures for
allocating staff work as follows:
1. Check the available staff and unassigned staff from finished shifts.
2. Read the queue statuses for both sides and add/remove staff based on the policy
provided by airport operation management.
3. Move staff between outbound and inbound because the queues exceed their limit and
there is no staff available. This can be done by continuously looking at the outbound
immigration queue and observing that if the queue length at outbound immigration
exceeds the given policy and there is more than one staff member at inbound
immigration, then move staff from inbound to outbound, and vice versa, where:
𝑄𝐼𝑚𝑚𝑖_𝑖𝑛𝑏𝑜𝑢𝑛𝑑 : Queue length for inbound immigration processing point
𝑄𝐼𝑚𝑚𝑖_𝑜𝑢𝑡𝑏𝑜𝑢𝑛𝑑 : Queue length for outbound immigration processing point
𝑇ℎ𝐼𝑚𝑚𝑖_𝑖𝑛𝑏𝑜𝑢𝑛𝑑 : Threshold for inbound immigration queue
𝑇ℎ𝐼𝑚𝑚𝑖_𝑜𝑢𝑡𝑏𝑜𝑢𝑛𝑑 : Threshold for outbound immigration queue
𝑁𝐼𝑚𝑚𝑖_𝑠𝑡𝑎𝑓𝑓 : Number of Immigration staff available in the database of global array
𝐼𝑚𝑚𝑖_𝑆𝑡𝑎𝑓𝑓 : Immigration staff assigned 𝑗 = 1,2,3 …
𝐼𝑚𝑚𝑖𝑠𝑡𝑎𝑓𝑓_𝐼𝑛 : Inbound immigration staff
𝐼𝑚𝑚𝑖𝑠𝑡𝑎𝑓𝑓_𝑜𝑢𝑡: Outbound immigration staff
𝐼𝑚𝑚𝑖𝑠ℎ𝑖𝑓𝑡__𝑠𝑡𝑎𝑟𝑡 : Start shift of immigration staff
𝐼𝑚𝑚𝑖𝑠ℎ𝑖𝑓𝑡__𝑒𝑛𝑑 : End shift of immigration staff
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 155
Figure 5 - 45: flowchart algorithm of immigration resource allocation
Chapter 5: A Framework for Sharing Staff between Outbound and Inbound Airport Processes. 157
5.5 CHAPTER SUMMARY
This chapter introduced the extension of an international terminal simulation model of
inbound flow processes. The model considered the main inbound processes and transit
processes, including passengers disembarking, security screening for transit passengers,
immigration, and quarantine. The ExtendSim software was utilised to model the inbound flow
processes, and these processes were built on the basis of hierarchy blocks.
Furthermore, this chapter presented a novel integrated simulation model, ARM, for an
entire international airport terminal model. The development model, for the first time, clearly
presents the dynamic resource allocation approach to the overarching model, which has helped
the model to be more accurate and mimic the real-life scenarios in the international airport
terminals. The staff can be utilised based on the selected method, either schedule-based or
demand-based, and can be moved from land-side to air-side according to the given policy
reflected by the queue thresholds.
In the next chapter, a case study is conducted to demonstrate the capability of the
simulation model and how well it reflects real-life scenarios. The case study is conducted by
obtaining actual data from King Khalid International Airport (KKIA), Riyadh, Saudi Arabia.
158 Chapter 6: Case Study - Validation of the Simulation Model
Chapter 6: Case Study - Validation of the
Simulation Model
6.1 INTRODUCTION
This chapter demonstrates that the proposed simulation modelling approach can
accurately represent an actual airport terminal. As discussed, airport terminals are a
dynamic environment involving several independent and interconnected sub systems
that are in communication with each other such as X-ray inspection, check-in, and
passport control facilities (Yamada et al., 2017). This chapter’s validation activities
were confirmed by comparing simulation results with the real data provided by King
Khalid International Airport (KKIA) – Riyadh, Saudi Arabia. Several runs were made
to determine the stochastic variability of the model. In this case study, four types of
simulation outcomes including the average and maximum waiting time in the queue
and the average and maximum cycle time at each departure facility were considered.
The given data only concerns departure processes including demand
characteristics and operational characteristics such as the airport flight schedule,
processing time, and number of service counters for processing points. Hence, the
validation processes take into consideration the outbound simulation model discussed
in Chapter 3, without the interaction with the inbound passengers
The King Khalid International Airport (KKAI) is the second largest airport in
the Kingdom of Saudi Arabia after King Abdul-Aziz in Jeddah. It is located 25 km
north of the capital city of Riyadh (Almuharib, 2014). The airport contains four
international passenger terminals, of which Terminals 1 and 2 are currently in use.
Terminal 1 is used for all international flights excluding those operated by Middle East
Airlines, Air France in addition to Saudia and Flynas use Terminal 2. Figure 6-1
displays an aerial view of the layout of the airport including terminal 1 and 2, which
are the first two from the top.
Chapter 6: Case Study - Validation of the Simulation Model 159
Figure 6 - 1: The terminals and runways of the King Khalid international airport.
According to collected statistics, KKIA has observed a steady increase in the
numbers of passengers traveling by air over the last 10 years. In 2016, the airport
handled more than 23.4 million passengers, as shown in Figure 6-2. Passengers can
access the airport by three transportation modes: the first type is a private car with
55.5% of the total of travellers and the second is a taxi with 42.2%. The third type is
the Saudi Public Transit Company, known as SAPTCO; this company transported only
2.3% (Alhussein, 2011).
Figure 6 - 2: Passenger movement numbers at KKIA from 2005-2016 (Statista, 2019)
160 Chapter 6: Case Study - Validation of the Simulation Model
Both terminals have the same layout, consisting of two levels where level one
deals with the inbound processes and level two with the outbound processes. By
comparing the design of KKAI terminals with Brisbane International Airport,
departing passengers can reach the departure level through elevators that take the
passengers from the basement level via the arrival level. The outbound processes start
with the security check of luggage by an X-ray machine.
The second process is dropping bags onto a conveyor belt to transfer them to
assigned flights and get checked in. When passengers complete the check-in process
they must pass through passport control counters prior to undergoing the security
screening process. Passengers then enter a departure holding area to await flight
boarding. Figure 6-3 illustrates in greater detail passenger flow types in KKIA. This
case study focuses on the international departure processes.
Figure 6 - 3: Scheme of passenger flow types at KKIA terminals (Kloosterziel et al., 2009).
Chapter 6: Case Study - Validation of the Simulation Model 161
6.2 KKAI OPERATIONAL DATA
To validate the developed simulation model presented earlier in this thesis,
operational data were taken from KKAI including flight schedule, processing time,
waiting time and physical resources. Flight schedule information is provided for each
terminal, including the number of flights, departure time and the number of passengers
for each flight. In Terminal 1 for instance, the number of flights is 38, the total number
of passengers is 5208 while the total number of passengers in Terminal 2 is 7501 per
day. Based on the given flight schedules and the total number of passengers travelling
per day for each terminal, this airport is classified as a small terminal and a medium
terminal respectively.
Other required input data of the airport’s terminal model are the data concerning
the service process and the physical resources of outbound processes. The KKIA
operations management was contacted to provide information associated with airport
terminal physical and processing time at each terminal facility. In KKIA, the
processing time and waiting data were collected by the KKIA operational staff through
observation of 15 passengers at each facility. The processing time data were analysed
using Extendsim statistical fit analyser in order to understand the processing time
distribution function for each service as the input for the simulation model. Figures 6-
4 and 6-5 illustrate the analysis of processing time data for Terminal 1 and Terminal
2.
To provide further analysis, Figures 6-4(a) and (b) present the scatter of the input
data and the fitted density of check-in facility of Terminal 1, where the x-axis of Figure
6-4(a) and (b) graphs represent the processing time in minutes. It can be observed that
the maximum processing time is 6.26 minutes while the mean (𝜇) 𝑖𝑠 2.15 minutes.
The processing time at check-in follows the Weibull distribution expression of
WEIBULL (0, 1.55, 2.4). Similarly, Figures 6-4(c) and (d) illustrate the scatter of the
input data and the fitted density of security screening facility of Terminal 1. The time
passengers need to be processed at security screening is illustrated as an Inverse
Weibull with a sample mean of 0.721 and standard deviation of 0.584. Additionally,
the distribution of passport control was found to follow Inverse Weibull as well, with
the sample mean of 0.521 and standard deviation of 0.409. In this airport, the greatest
amount of lost time incurred by passengers was at check-in, with the average
162 Chapter 6: Case Study - Validation of the Simulation Model
passenger waiting time in this domain being 15.95 minutes; while the average waiting
time for security and immigration is 1.25 minutes and 1.76 minutes, respectively.
The time that Terminal 2 passengers spent in being processed is different for all
facilities as shown in Figure 6-5. The time taken to process passengers at the check-in
facility is presented in Figure 6-5(b). The time is illustrated as triangular distribution
expression with a sample mean of 2.16 minutes and the standard deviation is 1.18.
Figure 6-5(c) presents the processing time input data for the security screening facility
of Terminal 2. The time passengers spent at security screening follows the same
distribution patterns as Terminal 1, which is Inverse Weibull with a sample mean of
0.22 minutes. Finally, the processing time at immigration has a triangular distribution
(0, 2.23, 0.15) as shown in Figure 6-5(f).
Chapter 6: Case Study - Validation of the Simulation Model 163
Figure 6 - 4: Processing time distributions for departure processes of Terminal 1 of KKIA
Check-in processing time, 15 samples mean = 2.152, SD= 1.490
Security screening processing time, 15 samples mean = 0.721, SD= 0.584
Immigration processing time, 15 samples mean = 0.521, SD= 0.409
Security screening processing time Inverse Weibull (0, 1.59, 2.52).
Immigration processing time Inverse Weibull (0, 1.99, 2.6).
Check-in processing time Weibull (0, 1.55, 2.4).
a
b
c
d
e
f
164 Chapter 6: Case Study - Validation of the Simulation Model
Figure 6 - 5: Processing time distributions for departure processes of Terminal 2 of KKIA
Check-in processing time Triangular (0, 5.11, 1.02).
Security screening processing time, 15 samples mean = 0.22, SD= 0.019
Immigration processing time, 15 samples mean = 0.76, SD= 0.51
Security screening processing time Inverse Weibull (0, 1.66, 9.35).
Immigration processing time Triangular (0, 2.23, 0.149).
Check-in processing time, 15 samples mean = 2.16, SD= 1.181
a
b
c
d
e
f
Chapter 6: Case Study - Validation of the Simulation Model 165
6.3 MODEL APPLICATION AND SIMULATION PROCESS
The developed simulation model is initialised and customised using the
available real data from the KKIA. The model is modified in terms of the operational
policies followed, and the physical structure of the airport terminals. Therefore, this
section presents the model application outcomes that concern the flows of departing
passengers through outbound processes in order to demonstrate the capabilities of the
model.
Figure 6-6 provides a visual representation of the KKIA departure flow
processes. Every processing point of the outbound system is represented by a module
in the model and each service facility contains a sub-process to perform for different
categories of passengers. Due to this complexity, too many processes are presented in
order to simplify the simulation process.
As for any international terminal, check-in process starts 3 hours before the
departure time where there are two lines to complete check-in, one at the left side of
the main entrance and the other one located at the right-hand side of the terminal. In
the model, each line of check-in service has 13 counters, three for business travellers
and 10 counters for economy travellers. The number of self-service check-in kiosks is
10 for each terminal, as shown in Figure 6-7.
In the simulation model, the default input parameters related to operational
policy were set based on the common practice of the airport provided by the
stakeholder. For example, a new economy check-in counter will be opened if the queue
length exceeds 20 passengers and is it closed if the queue has less than 5 passengers,
while the new business counter is opened when the number of passengers waiting in
the queue is three or more and closed if the queue is zero. Also, the priority for
passengers who arrive late at the airport check-in and are afraid to miss their flight is
considered in the model. After bags are checked-in and passengers receive their
boarding pass, they enter the second mandatory process of passport control, conducted
by immigration.
At the immigration process, there are 14 desks and six self-service kiosks
available for each terminal to process passengers. In the simulation model, seven desks
are available all the time and additional desks are opened if the queue is longer than
166 Chapter 6: Case Study - Validation of the Simulation Model
30 and closed when the queue is less than 5. Additionally, the input requirements can
be easily altered in order to evaluate ‘what-if’ scenarios (Sargent, 2013).
Figure 6 - 6: Flowchart of KKIA departure flow processes (researcher’s illustration)
Chapter 6: Case Study - Validation of the Simulation Model 167
In KKIA terminals, the security screening process is closely followed by the
immigration procedure. All passengers and carry-on bags will be checked. At each
terminal, there are six X-ray machines and five metal detectors. In the model, not all
the machines are available most of the time. It is assumed that three machines are
available all the time and additional ones are opened each time the queue length is
increased by 30 and closed if the number of passengers is five or less. Moreover, based
on similar airports, another assumption is made in regard to the percentage of the bags
that fail the X-ray; requiring the passengers to be requested to unpack their items,
which is 10% (Cheng, 2014; Kirk, 2013).
The performance metrics that are used to compare the outputs of the model with
real data are maximum and average waiting time and maximum and average cycle
time. Cycle time can be defined as the total amount of time including the waiting time
at a processing point and the process time. In the simulation model, the cycle time can
be calculated at each process as follows:
Use a block named “set block” in front of processing point.
Creating the attributes value, i.e. Immigration Cycle time.
use a block named information block to read the static information
regarding timing attributes as shown in Figure 6-8.
168 Chapter 6: Case Study - Validation of the Simulation Model
Figure 6 - 7: Process of calculating cycle time
6.4 SIMULATION RESULTS AND ANALYSIS
This section discusses the simulation results of the two terminals. In order to
demonstrate the model capacities, both terminals have been considered where each
has different input data such as flight schedule and processing time distribution as
shown in Table 6-1. Hence, the simulations were run independently, and the length of
each run is 1440 minutes. Four modules of the simulated international terminal model
will be presented for each simulation run. These modules are the passenger arrival, the
check-in process, the passport control facility and the security screening processes.
This section is structured as follows: section 6.4.1 presents the results of terminal one,
section 6.4.2 discusses the results of terminal two and section 6.4.3 provides a
comparison between the actual data with the simulation results.
Chapter 6: Case Study - Validation of the Simulation Model 169
Table 6 - 1: Summary of model default parameters at the KKIA international airport.
Parameters Values
Basic parameters:
Time to open check-in counters.
Time to start boarding to the flight prior to departure time
180 min
25 min
Processing parameters:
Open Economy check-in counter if the queue increases by
Open Business check-in counter if the queue increases by
Open security counter if the queue increases by
Open immigration counter if the queue increases by
Close Economy check-in counter if the queue is
Close Business check-in counter if the queue is
Close security counter if the queue is
Close immigration counter if the queue is
Passenger failure rate at metal detector
Percentage of passenger preforming self-service in Immigration
Passengers’ characteristics:
Percentages of business class passengers
Percentages of passengers using self-check-in
Percentages of passengers performing traditional check-in
20
3
30
30
5
0
5
5
10%
30%
15%
12%
80%
6.3.1 Description of Terminal 1 results
Since the passenger arrival pattern at the airport is essential, this is controlled by
the airport flight schedule. Based on the split of the model, passengers may arrive at
the airport using private car, taxi and public bus. Figure 6-9 shows the passengers’
arrival pattern per type of mode, demonstrating arrivals distribution over time (in 10
min time intervals). As discussed earlier, this arrival profile is based on the assumption
that 55.5% of KKIA customers use their own private car, 42.2% of the passengers use
taxis and only 2.3% of airport users would use the Saudi Public Transit (SAPTCO).
This figure illustrates a transition of the flight schedule as main input for the simulation
170 Chapter 6: Case Study - Validation of the Simulation Model
model. It includes the total number of outbound passengers that show up at the airport
during the day.
Figure 6 - 8: Arrival pattern and profile of Terminal 1 passengers.
There are three severe peaks that happen early in the day (e.g. 02:49-04:00 and
04:23-04:34 am), and another severe peak late in the day, i.e. 21:56-22:52 pm. There
are two smaller peaks, one in the morning (i.e. 07:15-08:00) and another one in the
afternoon (i.e. 15:36-16:00).
Figure 6-10 demonstrates simulation results referring to the three main outbound
processes (check-in, security screening and immigration). Check-in is managed by
airline policies. For example, different check-in rules can be applied by airlines based
on flight types, e.g. whether the flight is international or local. Moreover, travellers
are classified into those who operate by traditional check-in counters or self- service.
An additional classification exists, between economy passengers and business
class passengers. Figure 6-10(a) and (b) show the results of simulation over the time
period (day) where the x-axis demonstrates time in minutes (start from 0 to 1440
minutes) which corresponds to the day of simulation for the check-in facility. Figure
Chapter 6: Case Study - Validation of the Simulation Model 171
6-10(a) and (b) also illustrate the accumulation of passengers’ patterns during the day,
such as the number of passengers waiting in queue as well as the waiting time of
passengers in minutes, and both are displayed in the y-axes of the figures. It can be
observed that the maximum number of passengers waiting in front of economy
counters is 73 while the maximum number of business passengers is 4. Moreover, the
maximum time passengers wait in the economy queue is 48.77 minutes and the
maximum time business-class passengers wait is 3.63 minutes. However, the average
waiting time is 8.60 min, and 0.2 min for economy and business respectively.
The second important departure process is security screening; passengers have
to scan their items twice; one scan is located before the check-in process and the
second comes after the passport control facility. Figure 6-10(c) and (d) show the
results of simulating the security screening process in accordance with six available
security screening lanes and four random checks. The maximum waiting time is more
than 13 minutes, while the average waiting time is 2.67 minutes. Figure 6-10(c) and
(d) graphically present the accumulation of passenger patterns over time,
demonstrating several peaks occurring over the day with the period of these peaks
concentrating around 02:55-03:25 and 16:56- 17:23 and the maximum number of
passengers in the queue being 70. Finally, Figure 6-10(e) and (f) show the simulation
results for the immigration process, which is responsible for passport control. Figure
6-10(e) and (f) displayed patterns of accumulation of travellers represented by the
number of passengers waiting in queues and the maximum and average of waiting
times. The average waiting time at passport control is 2.2 minutes, while the maximum
waiting time exceeds 10 minutes. Several peaks can be seen and they mainly happened
in the same period as the peaks for security screening.
172 Chapter 6: Case Study - Validation of the Simulation Model
Figure 6 - 9: a, b Terminal 1 check-in process results; c, d Terminal 1 security screening process results; e, f Terminal 1 immigration process results
0 360 720 1080 14400
6.666667
13.33333
20
26.66667
33.33333
40
46.66667
53.33333
60
66.66667
73.33333
80
Time (min)
No.PaxPax In Queue for Check-in
Economy _Q_Leng Busines_Q_Lengt
0 360 720 1080 14400
6.25
12.5
18.75
25
31.25
37.5
43.75
50
Time (min)
WaitingTime(min)Waiting time for Check-in
Economy _Waiting BusinessWaiting
0 360 720 1080 14400
8.75
17.5
26.25
35
43.75
52.5
61.25
70
Time (min)
No.PaxPax in queue for security screeing
13.86407 370.3981 726.932 1083.466 14400
1.147892
2.295784
3.443676
4.591568
5.73946
6.887352
8.035244
9.183137
10.33103
11.47892
12.62681
13.7747
Time (min)
waiting time(min)waiting time for Security check
security waitng…
0 360 720 1080 14400
3.25
6.5
9.75
13
16.25
19.5
22.75
26
29.25
32.5
35.75
39
Time (min)
No.PaxPax in queue for immigration
Immig_queue_L SmartGate_Q_L
12.49861 369.374 726.2493 1083.125 14400
1.275665
2.55133
3.826994
5.102659
6.378324
7.653989
8.929654
10.20532
Time (min)
Waiting time(min)WaitingTime for immigration
Immig_Waiting
e a c
b d f
Chapter 6: Case Study - Validation of the Simulation Model 173
6.3.2 Description of Terminal 2 results
This section first discusses passenger arrival patterns at Terminal 2 of KKIA.
The second part of this section will discuss the simulation results for the main
departure facilities. Figure 6-11 presents the departing passenger arrival profile and
the distribution of entering Terminal 2 of KKIA. As explained earlier, the passenger
arrival pattern is controlled by the airport flight schedule. Two main peaks can be seen
during the day; one occurs early morning around 05:46-06:38 and another one occurs
in the afternoon between 13:04-14:48. Another three smaller peaks were observed;
two of them happened in the morning 08:43-09:05 and 10:40–11:10, and the last one
occurred in the evening around 21:57-22:27.
Figure 6 - 10: Arrival pattern of Terminal 2 passengers and entering Terminal 2 distribution.
Figure 6-12(a) and (b) illustrate simulation results during the day for the check-
in process, including the volume of passengers waiting in queue and waiting time spent
in queue.. The maximum waiting time reaches 14.29 minutes while the average
waiting time is 4.13 minutes. Furthermore, the maximum number of passengers
waiting in queue was shown at 360 minutes of x-axis of simulation time to be 43,
which aligns with the first peak of the departing passenger arrival profile.
174 Chapter 6: Case Study - Validation of the Simulation Model
Similar to the Terminal 1 security screening process, passenger baggage needs
to be inspected twice. Figure 6-12(c) and (d) presents the simulation results including
the number of passengers waiting in queue and the waiting time in minutes. The
maximum waiting time is 12.26 minutes, although the average waiting time is 1.77
minutes. There was a severe peak in the early hours of the day around 05:46-06:38,
lasting about a half-hour with a maximum passenger waiting time of 12.29 minutes.
Figure 6-12(e) and (f) illustrated the results of simulation for the passport control
process of Terminal 2. Two severe peaks happen in the early hours of the day around
02:30-03:00 and 05:46-06:38 and the number of passengers in the queue reaches its
maximum of 176 in the second peak. The average waiting time of passengers in front
of passport control is 5.25 minutes and the maximum waiting time is 17.18 minutes.
These results are aligned with the arrival patterns illustrated in Figures 6-9 and 6-11.
Chapter 6: Case Study - Validation of the Simulation Model 175
6.855429 365.1416 723.4277 1081.714 14400
1.786762
3.573524
5.360286
7.147047
8.933809
10.72057
12.50733
14.29409
Time (min)
WaitingTime(min)Waiting time for check-in
Economy _Waiting BusinessWaiting
0 360 720 1080 1440-1.453061
13.38027
28.21361
43.04694
57.88027
72.71361
87.54694
102.3803
117.2136
132.0469
146.8803
161.7136
176.5469
Time (min)
No.PaxPax in Queue for immigration
Immig_Q_length
19.25026 374.4377 729.6251 1084.813 1440-0.1258835
1.306041
2.737965
4.169889
5.601814
7.033738
8.465662
9.897587
11.32951
12.76144
14.19336
15.62528
17.05721
Time (min)
WaitingTime(min)Waiting time for immigration
Immig_waitingTi Immig_SmartGate
21.30821 375.9812 730.6541 1085.327 14400
1.021284
2.042568
3.063852
4.085136
5.10642
6.127704
7.148988
8.170272
9.191555
10.21284
11.23412
12.25541
Time
waiting time(min)Secruity screening waitingTime
security waitng…
e a
0 360 720 1080 14400.2234043
5.473404
10.7234
15.9734
21.2234
26.4734
31.7234
36.9734
42.2234
Time
No.PaxPax In Queue for check-in
Business_Q_leng Economy _Q_Lengt
0 360 720 1080 14400
4.5
9
13.5
18
22.5
27
31.5
36
40.5
45
49.5
54
Time (min)
No.PaxPax in queue for security screening
Security Q_leng
c
b d f
Figure 6 - 11: a, b Terminal 2 check-in process results; c, d Terminal 2 security screening results; e, f Terminal 2 immigration process results
Chapter 6: Case Study - Validation of the Simulation Model 177
6.3.3 Results analysis and discussion
In this section, we will discuss the validation process of the developed model
using empirical and statistical validation. Statistical validation is made by comparing
the simulation results with the observed data from the airports, as collecting real data
from airports is time consuming and an extremely intensive job (Livingstone, Popovic,
Kraal, & Kirk, 2012). Additionally, there is a limitation in the required data related to
the average waiting time and the total of cycle time at each outbound processing point
(Cheng, 2014). Because of the limited actual statistical data available for specific
airport terminals, past studies could only validate limited outcomes of the simulation
with available data of airports in terms of passenger flow. This is done to demonstrate
that how simulation model can utilised to analyse the passenger flows in an
international airport as a response to question one.
As explained earlier, two different terminals of KKIA were simulated, each with
different flight timetables as well as different time distributions. Table 6-2 compares
the simulation results and real data of Terminal 1 obtained at each processing point.
There is high variation between the average waiting time of the simulation and actual
data, while the variation is slightly less in the average cycle time, because of less
processing time, especially in the check-in and security screening facilities. However,
the model can provide better results and reflect the actual situation in the immigration
process. Since this terminal operates international flights, there are variances in
policies performing each flight and its passengers; for instance, there might be an extra
check and some might have stricter security check-ups (Ma, 2013). Furthermore, this
terminal provides an extra baggage check before check-in, which require more time
compared with Australian airports.
For further analysis, the same processes have been conducted for Terminal 2 of
KKIA. Table 6-3 illustrates the comparison between the actual data and simulation
results in terms of the average waiting time and total cycle time at each processing
point of Terminal 2
178 Chapter 6: Case Study - Validation of the Simulation Model
Table 6 - 2: Comparisons of waiting time in queue and cycle time at check-in, security and immigration
between the simulation data and real time data of Terminal 1.
Domain
Terminal 1
Waiting time (min) Cycle time (min)
Actual Simulation Relative error Actual Simulation Relative
error
Check-in
Max 57.88 48.77 18.67% 64.14 56.31 13.91%
Average 15.95 8.60 85.46% 18.103 11.38 59.6%
Security
Max 3 13.77 78.21% 4.54 20.22 77.54%
Average 1.25 2.67 53.18% 1.97 3.87 49.10%
Immigration
Max 7 10.21 31.4% 17.4 28.81 40%
Average 1.76 2.2 20% 2.76 2.84 2.81%
The results of the average waiting and average cycle times at both check-in and
security screening facilities are slightly better in terms of less variation compared with
Terminal 1 results. Though in the case of the immigration facility, the variation is high
compared with the results of Terminal 1.
Table 6 - 3: Comparisons of waiting time in queue and cycle time at check-in, security and immigration
between the simulation data and the real time data of Terminal 2.
Domain
Terminal 2
Waiting time (min) Cycle time (min)
Actual Simulation Relative error Actual Simulation Relative
error
Check-in
Max 9 14.29 37.02% 18.37 17.24 6.55%
Average 3.68 4.13 10.90% 6.16 6.54 5.81%
Security
Max 3 12.26 75.53% 10 17.70 43.50%
Average 0.88 1.77 50.28% 1.55 2.02 23.27%
Immigration
Max 5 17.18 70.90% 9.16 18.29 49.92%
Average 1.56 5.24 70.22% 2.78 6 53.67%
It is believed that the developed model would be more appropriate for such
airports as Brisbane International Airport, since comparisons of the results of the
simulation model with the observed data collected from Brisbane International Airport
Chapter 6: Case Study - Validation of the Simulation Model 179
by Kirk (2013), were within acceptable differences (Table 6-4). According to Cheng
(2014), with a benchmark showing differences between the average actual time and
the average of simulated time being less than 2 minutes, the model would reflect the
actual situation except for the average cycle time of the security screening facility.
Despite limitations of the model applicable to KKIA airport, given the flight schedule,
the simulation model can provide valuable information for airport management about
any potential congestion as well as the peaks aligning with the departing passenger
arrival patterns shown in Figures 6-9 and 6-11. In order to address the limitations of
the developed simulation model to be more applicable in KKIA international airport,
further improvement is required. For example, other processes such as an extra
baggage check occurred before check-in processes should be considered
Table 6 - 4: Comparisons of waiting time in queue and cycle time at check-in, security and immigration
between the simulation data and the real time data at Brisbane International Airport.
Domain
Brisbane Airport
Waiting time (min) Cycle time (min)
Actual
(Kirk, 2013) Simulation
Relative
error
Actual
(Kirk, 2013) Simulation
Relative
error
Check-in
Max 42.81 41.68 2.71% 53.56 46.34 15.58%
Average 12.88 12.06 6.80% 16.65 16.25 2.46%
Security
Max 17.09 26.21 34.80% 21.06 27.8 24.24%
Average 3.75 4.02 6.72% 6.88 4.7 46.38%
Immigration
Max 15.46 25.68 39.79% 18.58 26.17 29%
Average 4.8 5.67 15.34% 6 6.83 12.15%
Ma (2013) argued that the validation of passenger flow at airports depended on
three main elements: passenger flow speed within the terminal, immediate occupancy
by passengers at particular areas in the terminal, and the routing decision by
passengers. Thus, to have more accurate validation, video recording might be used to
collect relevant data such as the waiting time and the cycle time at each terminal
facility. Video cameras can obtain samples and volumes of samples more efficiently
and accurately, because they often record a full day, week or even a month of data.
180 Chapter 6: Case Study - Validation of the Simulation Model
6.5 CHAPTER SUMMARY
In this chapter, two case studies of passenger flow simulation in the airport of
King Khalid International Airport in Riyadh have been demonstrated. Due to the
available data, the main focus was the outbound process including check-in, security
screening and immigration. The first study was conducted at Terminal 1 of KKIA with
its own flight schedule. It could be clearly seen from the departing passengers’ arrival
pattern graph that there are several peaks occurring during the day. The second study
was undertaken at Terminal 2 of KKIA. Four categories of outputs were generated;
average/maximum waiting time and average/maximum cycle time. In both studies, it
can be observed that bottlenecks at departure facilities occurred in conjunction with
peaks in departing passenger arrival patterns. It could be clearly seen that simulation
results align with the arrival pattern.
However, by comparing the results of a simulation model with actual data of
both terminals, results showed that there is great variation in the check-in and security
average waiting times of Terminal 1, around 85% compared with the real scenario, and
this variation decreased to 59% in the cycle time results for the same facility. The
results of the immigration facility had the lowest variation at about 20% and 2.81% for
the average waiting time and cycle time, respectively. On the other hand, the
simulation results of Terminal 2 showed that check-in average waiting time and
average cycle time have lower variation around 10% and 5.81%, respectively.
Contrary to the results of the immigration facility of Terminal 1, the variation in the
results of average waiting time and average cycle time is high at about 70.22% and
53.67%, respectively.
Comparison of the results of the proposed simulation model with actual data
showed that the model is more applicable for local airports, such as Brisbane
International Airport, than external airports such as KKIA. The results of the model
might be improved by changing the input requirements and running ‘what-if’
scenarios; since airports are exposed to external effects and the developments of air
traffic, it is better to repeat and validate the results of the study frequently (Rauch &
Kljajić, 2006). According to Sargent (2013), there is no particular group of
experiments that can be applied easily to find the model correctness. Conversely, the
model provided sufficient accuracy for local international airports such as Brisbane,
where results demonstrated that the simulation reflects the actual situation. It is known
Chapter 6: Case Study - Validation of the Simulation Model 181
that the accuracy of the model for a particular case does not guarantee that the same
model can be valid everywhere in its related domain (Sargent, 2013).
Chapter 7 of this thesis presents the integrated simulation model within the
advanced resource management (presented in Chapter 5) and demonstrates how the
proposed model can be used to investigate the effect of different staff allocation
techniques on both sides of the airport terminal (outbound and inbound) processes.
The effects of these staff allocation methods can be understood by comparing the
simulation results under different settings or scenarios.
182 Chapter 7: Application of Advanced Resource Management (ARM)
Chapter 7: Application of Advanced
Resource Management (ARM)
7.1 INTRODUCTION
Passenger satisfaction has become a significant concern for modern airports. In
daily operations, having efficient and effective resource allocation methods in place
can significantly improve the performance of international terminal operations and
enhance passenger satisfaction. Despite recent research on developing resource
allocation models for one airport system domain (e.g. inbound or outbound), there is
little research on the management and allocation of staff in entire international
terminals. For example, if more personnel are assigned to immigration counters for
passport checking from another terminal domain, the process can be faster and high
levels of service will be offered to passengers. This movement of personnel, however,
might lead to a large number of passengers accumulating in the other domains which
could negatively affect levels of service. Thus, there is a need for a decision support
tool for airport terminal planning and operations management to significantly enhance
the efficiency of the overall system (Manataki & Zografos, 2009).
The Advanced Resource Management (ARM) algorithms presented in Chapter
5 are added to the simulation model to study the variable and complex environment of
operational policy. The ARM was developed to be an integrated system used for
arranging resources, identifying the proper resource and allocating them throughout
the model (Imagine That Inc, 2013). It was used to investigate the influence of
different staff allocation techniques on both sides of the airport terminal (outbound
and inbound) processes, by doing this question three will be answered. The effects of
these staff allocation methods can be understood by comparing the simulation results
under different settings or scenarios. This is done by analysing the results obtained
from allocating staff according to scheduled based allocation (static approach) and
demand-based allocation (dynamic approach) under the same conditions and input
variables. The overall objective of the developed ARM is to enhance passenger
satisfaction through reasonable wait time processing at the lowest cost possible
(minimal staff hours). The analysis of the proposed approach is based on processing
Chapter 7: Application of Advanced Resource Management (ARM) 183
activities including check-in, inbound and outbound security control, inbound and
outbound immigration, and quarantine and boarding procedures.
Moreover, this chapter discusses an integrated analytical model for the
optimisation of resources of airport terminal. The objective of the developed
mathematical model is to minimise the cost of waiting time in queues by determining
where the additional resources should be placed. In this mode, there are different
resources types, each with different amounts, hence, another aim of the proposed
model is to obtain resources at minimum total cost.
Section 7.2 demonstrates staff management techniques including the general
inputs of the experiments. Section 7.3 analyses the influence of static and dynamic
allocation methods on the performance of the international terminal. Section 7.4
presents the best policies for dynamic allocation at airport activities. Finally, the
analytical model is discussed and introduced in section 7.5.
7.2 OVERVIEW OF AIRPORT RESOURCE MANAGEMENT
The focus of this thesis is the effective management of passenger flows inside
the airport terminal by improving resource allocation. For example, opening new
counters at the security control and adding extra staff to prevent long queues. This
research aims to identify the best possible techniques and policies of staff allocation
to ensure the desired balance between demand and service quality. Since the terminal
is a highly complex system, with two significant types of passenger flows processes,
the development of a decision-support tool for managing overall staff allocation
processes is challenging. To simplify this issue, the model has been divided into three
sub-models. The first sub-model simulates outbound passenger flow processes as
illustrated in Chapter 3. The second sub-model simulates inbound passenger flow
processes as explained in Chapter 5. The last sub-model involves developing novel
algorithms for resource allocation for both inbound and outbound passenger processes.
It is expected that this model will provide more accurate outcomes in representing
flows of arriving and departing passengers and service processes. The model can be
used to explore a wide range of what-if scenarios which helps with more effective
decision-making in airport terminal planning, operations and management. By doing
this question one can be addressed.
184 Chapter 7: Application of Advanced Resource Management (ARM)
7.2.1 Model demonstration
The proposed model is developed to accurately mimic real-world situations
since terminal operations are dynamic and involve a range of services, e.g. check-in,
passport control, boarding pass control, security screening. To demonstrate the
model’s capability, a set of experiments was conducted to analyse staff allocation
methods under the same conditions and input data. The following phases were
performed:
i. Two experimental scenarios, each with a total of 81 simulation runs, were
conducted to test the variable of staff number for each processing station.
Experiment 1 considers the base case (static method) of allocating staff,
while experiment 2 allocates the terminal staff dynamically, i.e. staff will
be assigned when and where needed.
ii. To obtain insight into stochastic variations, the simulation was run more
than 200 times for each method (static and dynamic) under the same input
data.
7.2.2 General input data
For comparative purposes, the experimental scenarios were conducted under the
same conditions. The first type of data is the flight schedule for both types of flight
departure and arrival. The second type of data relates to the operational characteristics
of the system, for example, the processing distribution at the various airport processing
points and the number of checking points available. Table 7-1 summarises the
operational input data.
Chapter 7: Application of Advanced Resource Management (ARM) 185
Table 7 - 1: Summary of common operational input data for the experiments
Airport
domain
Flow Direction of
PAX
Staff Processing time
Check-in Outbound 8-10 Delay time at check counters =
0.2 min ∗ # of bags
Security
screening
Outbound/inbound 3-5 ~ Tri (0.2, 0.5, 0.75)
Immigration Outbound 6-8 ~ Tri (0.5, 1, 2)
Boarding Outbound Serviced by
airline staff
15 PAX /min
Baggage
collection
Inbound - ~ Norm (10, 3)
Quarantine
Declare
No-
declare
Inbound 12-16 ~ Uniform (1, 5) for declaration and
~ Tri (0.5, 1, 0.75) for nothing to
declare
7.3 SIMULATION RESULTS AND ANALYSES
This section discusses the results of the initial experiments conducted to
demonstrate the developed dynamic staff allocation and to ensure reliability of the
results. Since there are four types of staff including airline, security, immigration and
quarantine, the simulation was run with various staffing levels for each process to
ensure that the terminal is operated with all possible scenarios as shown in Table 7-1.
The results were collected from 81 multi-simulation runs for the two types of
allocation methods: (1) allocating staff according to static base, (2) allocating staff
dynamically—staff allocated when needed. For each simulation run the KPIs
considered in this study were recorded for each processing point, both outbound and
inbound. The KPIs include maximum/average queue length, maximum/average
waiting time, number of late flights/average time delay for late flights and total staff
hours. The output of each scenario was recorded using the ExtendSim database global
array then exported to Excel for analyses as illustrated in Figure 7-1.
186 Chapter 7: Application of Advanced Resource Management (ARM)
Figure 7- 1: Example of ExtendSim database for output data
The airport terminal is operated as one single system with multiple elements
located in the different types of outbound and inbound passenger flow processes. For
this study, staff types were grouped into four different groups: airline staff,
immigration staff, security staff and quarantine staff. The number of scenarios was set
based on the Cartesian product of the staff sets which is defined as (Imrich, Klavžar,
& Rall, 2008):
𝐴 × 𝐵 × 𝐶 × 𝐷 = (𝑎, 𝑏, 𝑐, 𝑑)|𝑎 ∈ 𝐴, 𝑏 ∈ 𝐵, 𝑐 ∈ 𝐶, 𝑑 ∈ 𝐷.
It is assumed that set A = airline staff = 8, 9, 10, set B = Security staff = 3,
4, 5, set C = Immigration staff = 6, 7, 8, set D = Quarantine staff = 6, 7, 8. Since
each set has three elements, the Cartesian products of these four sets have 81 ordered
pairs. The same order of pairs will be used for both experiments for two reasons, (i) to
understand significance of the dynamic allocation approach and how it improves the
efficiency of all processes, and (ii) to obtain insights into the best policy for airport
operations and management.
7.3.1 Static Allocation Base Case method
This section discusses the simulation results from the Static Allocation Base
Case (SABC) method. The SABC method has many variations and is projected to
Chapter 7: Application of Advanced Resource Management (ARM) 187
provide shorter waiting time for passengers as the allocation procedures of SABC are
not controlled by adding and removing staff polices discussed in section 5.4.2.
Inefficient use of airport resources and potentially significant financial losses due to
greater operating hours represented by staffing hours usually result from this method.
7.3.1.1. Check-in and boarding
The effects of SABC on check-in and boarding were investigated with respect
to the average passenger waiting time in queues and the total staffing hours at check-
in counters to serve passengers over 24 hours. Figure 7-2 demonstrates the average
waiting time at check-in counters and the total staffing hours for all 81 possible
scenarios. There is fluctuation in economy passenger average waiting time values over
the first 28 scenarios when there are only eight staff available. The average waiting
time then stabilises for the remaining scenarios. The average waiting time spent at
check-in lies between 5-7.6 minutes. There was also a slight increase in the number of
staff working hours at check-in for the same scenarios, where the average and
minimum working hour’s values are 889 and 772 hours, respectively.
Figure 7- 2: Check-in average waiting time using the SABC method
Figure 7-3 presents the status of boarding procedures employing the SABC
method. Two variables were considered: the number of delayed flights and average
time delay for these flights. In general, the time from the start of boarding to flight
departure is about 30 minutes (Cheng, 2014). Hence, any flight departing more than
188 Chapter 7: Application of Advanced Resource Management (ARM)
30 minutes after the original time is considered a delayed flight. This figure also
demonstrates considerable fluctuation in the number of delayed flights and the average
delay time. The number of delayed flights reached the peak of 18 flights in the 19th
simulation run with an average delay time of 28 minutes.
Figure 7- 3: Influence of the SABC method on boarding procedures
7.3.1.2. Security domain
Figure 7-4 illustrates the impacts of SABC on security screening processes. It
outlines the average waiting time and the total staffing hour status of outbound and
inbound security processes. Similar patterns were found as for boarding procedures,
especially outbound average waiting time and staff hours. There was significant
fluctuation in the outbound average waiting time—reaching a maximum of 21.5
minutes if there are 3 staff members available and a minimum of 1.5 minutes when
there are 5 people available. The inbound average time remained stable for all
scenarios. The maximum total staffing hours stabilised at 120 hours if there are 5 staff
members available and has dropped one and half time to 73 when the system is
operated with only three people.
Chapter 7: Application of Advanced Resource Management (ARM) 189
Figure 7- 4: Security screening average waiting time using the SABC method
7.3.1.3. Immigration domain
This section analyses the impacts of the SABC method on immigration
operational performance. Figure 7-5 demonstrates that the average waiting time for
passengers to be processed by outbound immigration staff reached its highest point of
3.5 minutes in scenarios 1-10, 28-36 and 55-64 when the total number of staffing hours
was 287 hours with 3 staff members assigned to serve passengers. There was high
variation in the average waiting time at outbound immigration due to the irregular flow
rate caused by other processing points such as check-in or security. The average
waiting time of inbound immigration remained stable given the same number of
available staff.
190 Chapter 7: Application of Advanced Resource Management (ARM)
Figure 7- 5: Immigration average waiting time using the SABC method
7.3.1.4. Quarantine domain
Figure 7.6 illustrates the performance of the quarantine system in terms of the
average waiting time. There were consecutive peaks in average waiting time for
quarantine processing when only six staff members were available. The maximum
average waiting time for declaration stations was 57.51 minutes while the shortest wait
time was 23.21 minutes. For non-declaration processing stations, the maximum
average waiting time was 55.88 minutes and the shortest time was 5.25 minutes.
Figure 7- 6: Quarantine average waiting time using the SABC method
Chapter 7: Application of Advanced Resource Management (ARM) 191
7.3.2 Dynamic resource allocation method
This section describes the results from using the dynamic resource allocation
method and compares the results with the SABC method results discussed in section
7.3.1. Applying the dynamic allocation method enhances the efficiency of airport
terminal operational processes. As it can strike a balance between demand and staffing
hours—staff will be allocated to a particular workstation based on the given airport
policy.
7.3.2.1. Check-in and boarding domain
Figure 7-7 displays the average waiting time status at check-in facilities when
the dynamic based approach is used. The dynamic resource allocation method has a
significant influence on the performance effectiveness of the check-in domain. The
average waiting time is a little longer than the SABC, this is because of the given
policy including adding and removing staff based on the queue threshold. The
economy average waiting time decreased gradually from around 16.5 minutes to 13.5
minutes by increasing airline staff from 8 to 9 people and from 13.5 minutes to 11.5
minutes by increasing staff from 9 to 10. Further analysis of the policy of allocating
and reallocating staff will be investigated in section 7.4.
The total staffing hours stabilised with a peak of 326 hours while the lowest
operating hours was 258 hours. These staff operating hour levels are half that from
using the SABC method.
Figure 7- 7: Check-in average waiting time using the dynamic resource allocation method
192 Chapter 7: Application of Advanced Resource Management (ARM)
Figure 7-8 illustrates the impacts of the dynamic resource allocation technique
on boarding time. The dynamic method provided better results in regards to fewer
delayed flights. There was little variation in the number of delayed flights over the
first 28 scenarios when eight airline staff members were allocated to boarding
processes. When more staff were added, the number of delayed flights experienced a
slight drop followed by considerable variation. The highest number of delayed flights
was 13 with 8 staff members available. The maximum delay time for delayed flights
was around 18 minutes with a minimum of 4 minutes.
Figure 7- 8: Influence of dynamic resource allocation method on boarding procedures
7.3.2.2. Security domains
Figure 7-9 shows the average waiting status versus the operation time at security
screening for both outbound and inbound flow processes. Applying the dynamic
allocation approach resulted in stabilised average waiting times for outbound and
inbound security. The average waiting times for outbound and inbound security were
around 5-8 minutes and 7-9 minutes, respectively. The maximum number of staffing
hours was 95 hours and the minimum as 46 hours – both lower than when using the
SABC method (section 7.3.1.2). These results are based on the given conditions, such
as adding and removing staff, if the queue reaches its upper or lower limit. Results can
be improved through performing what-if analysis scenarios to select the best security
system operation policy.
Chapter 7: Application of Advanced Resource Management (ARM) 193
Figure 7- 9: Security screening average waiting time using the dynamic resource allocation method
7.3.2.3. Immigration domain
Figure 7-10 illustrates the influence of the dynamic allocation approach on
immigration processes. There was moderate fluctuation in the average waiting time in
outbound and inbound immigration queues ranging between 6-8 minutes on both
sides. The highest value of staffing hours is 76 hours, while the minimum value is 63
hours. These numbers for both outbound and inbound processes are much lower than
those generated using the SABC method (section 7.3.1.3). Further analysis of the
impacts of different sharing policies will be presented in section 7.4.
Figure 7- 10: Immigration average waiting time using the dynamic resource allocation method
194 Chapter 7: Application of Advanced Resource Management (ARM)
7.3.2.4. Quarantine domain
In the quarantine domain, dynamic and static based scenarios have an almost
similar impact. Figure 7-11 demonstrates that there is congestion in the non-
declaration processing points. The longest average waiting time for declaration is 59
minutes if there are only 5 staff members available. If the upper limit number of staff
(8) was utilised, the average waiting time decreased sharply to below 30 minutes.
Figure 7- 11: Quarantine average waiting time using the dynamic resource allocation method
7.3.3 Comparison of overall impact of static and dynamic allocation models
After analysing each processing point individually in sections 7.3.1 and 7.3.2,
the dynamic approach was found to provide better results because it mimics real-life
scenarios including adding and removing staff and sharing staff between related
processes.
This section compares the results of total average waiting time and the total staff
hours using both methods of allocating staff as shown in Figures 7-12 and 7-13. SABC
results show considerable variation in regard to the outbound average waiting time
compared with the dynamic allocation method under the same inputs, especially, the
number of personnel available. The dynamic approach can significantly impact the
total staffing hours (time spent serving passengers), halving the hours produced by the
SABC method. The proposed dynamic algorithms also have fewer variations
associated with the total staff hours where the minimum number of staffing hours is
Chapter 7: Application of Advanced Resource Management (ARM) 195
625.78 hours and the maximum is 777.15 hours. In comparison, the SABC minimum
number of working hours is 1432 hours and the maximum is 1781 hours.
After employing the dynamic resource allocation method, the total average
waiting time in the outbound system is stable over all scenarios with a maximum of
24.52 minutes and a minimum of 17.38 minutes. The most interesting finding was that
the check-in process plays a significant role in the performance of the entire outbound
system in the case of the dynamic allocation method. According to the literature, the
check-in process is considered the major factor causing delays and congestion at
airport terminals with more than 60% of the total time spent at check-in (Guizzi et al.,
2009; Ma, 2013; Park & Ahn, 2003; Schultz & Fricke, 2011).
Figure 7- 12: Static allocation method results
Figure 7- 13: Dynamic resource allocation method results
196 Chapter 7: Application of Advanced Resource Management (ARM)
7.3.4 Identify the variation of static and dynamic allocation methods
In order to understand the variations of both methods of allocating airport
terminal staff, the model was run over 200 times using the same operational inputs
data described in section 7.2.2 and with a fixed number of staff. Figure 7-14
demonstrates the mean average waiting time and variance using SABC methods while
Figure 7-15 shows the mean average waiting time and variance for the dynamic
resource allocation method. The variance of the SABC has is higher than that of the
dynamic resource allocation method. In addition, the variations in the variance of the
dynamic method occurred around the mean average waiting time while that of the
SABC did not.
Figure 7- 14: Variation in the SABC method
Figure 7- 15: Variation in the dynamic resource allocation approach
Chapter 7: Application of Advanced Resource Management (ARM) 197
However, having the same input, results showed that SABC has lower mean
and variance values, around 4 and 8, respectively, for the first 13 scenarios then
dropped slightly for the rest of the scenarios (mean 2.587, variance 5.853). The mean
and variance of the dynamic allocation approach were 8.223 and 10.206, respectively.
7.4 DYNAMIC APPROACH DEMONSTRATION
The dynamic algorithms presented in section 5.4 have been used to mimic real-
life personnel planning for an international terminal. To demonstrate the approach
capability, a set of experiments using “what if” scenarios were conducted considering
various policies in regard to adding/ removing staff and sharing staff between two
processes. These experiments can demonstrate how the developed dynamic models
improve the performance of the system and how it can be optimally operated under
different operational policies. This investigation can provide a deeper understanding
of best strategy to enhance system performance.
Initially, the queue threshold controls the adding and removing staff rules at each
terminal element. The second phase of sharing staff between related processes is
governed by priority conditions. This is transformed into model input as described in
section 5.4.3. In the next two sections, two more features of the dynamic approach will
be discussed. First, section 7.4.1 demonstrates the impacts of adding and removing
policies on international terminal facilities, followed by section 7.4.2 that investigates
different policies for sharing staff.
7.4.1 Adding and removing staff polices for non-integrated processes
Figure 7-16 illustrates the influence of staff adding and removing policies on
check-in performance, including business and economy counters. Different policies
have been used to evaluate the best policy for adding and removing employees at
check-in. This experiment started with initial operational policies, for example, to add
more staff at business and economy counters when the queue threshold is 5 and 10
passengers, respectively, and to remove one staff member when queue length becomes
2 for business and 10 for economy passengers. Hence, scenarios are combinations of
the following sets of policies:
i. Sets of adding one staff at business check-in if queue length =
1, 2, 3, 4, 5, 6, 7, 8
198 Chapter 7: Application of Advanced Resource Management (ARM)
ii. Sets of removing one staff at business check-in if queue length =
0, 1, 2, 3, 4, 5, 6, 7
iii. Sets of adding one staff at economy check-in if queue length =
10, 20, 30, 40, 50, 60, 70, 80
iv. Sets of removing one staff at economy check-in if queue length =
5, 10, 15, 20, 25, 30, 35, 40
According to Kazda and Caves (2015), the satisfactory length of queue time at
economy check-in is 12 minutes and 3 minutes for business class. Kirk (2013)
observed that 50 of the 71 passengers observed spent the longest amount of time at
check-in with an average time of 17 minutes. Individual times ranged from 2 to 54
minutes.
Figure 7-16 shows that the satisfactory level can be reached in the first three
scenarios with the rule of adding one economy staff member if the queue threshold is
10, 20 and 30, and the policy of queue threshold of other terminal domains is fixed
with the initial operational policies. It occurred again for scenarios 35 and above with
the policy of adding and removing staff for business counters at thresholds of 8 and 0,
respectively, and for economy thresholds of 30 and 5, respectively. The lowest
economy maximum and average waiting times were 48.016 and 7.917 minutes,
respectively, in the first scenario with the initial operational policies discussed earlier.
The lowest maximum and average waiting time in business class was 1.78 and .012
minutes, respectively, for scenario 51 (adding and removing staff set at 1 and 0 for
business class, respectively, and 10 and 40, respectively for economy). Hence,
selection of the best policy is the set of rules that meet the acceptable waiting times
for both business and economy counters.
Chapter 7: Application of Advanced Resource Management (ARM) 199
Figure 7- 16: Check-in facility results of adding and removing staffing policies
As explained in Chapter 5, another non-integrated process of an international
airport terminal domain is the quarantine domain located in inbound flow processes.
In quarantine, 65 different policies were tested to understand the best policy for
optimal quarantine operation in both lanes of quarantine sub-processes; declaration
and nothing to declare lane. The initial operational policy of adding quarantine staff
for this experiment is 10 and 30 passengers for declaration and nothing to declare
lanes, respectively. The initial policy for removal is 10 and 10 passengers for
declaration and nothing to declare lanes, respectively. The same number of policies
used for check-in were used for this experiment. However, there are some differences
regarding rules for the number of passengers waiting in the queue. The following is
the sets of policies:
Sets of adding one staff at declaration if queue length =
10, 20, 30, 40, 50, 60, 70, 80
Sets of removing one staff at declaration if queue length =
5, 10, 15, 20, 25, 30, 35, 40
Sets of adding one staff at nothing to declare if queue length =
10, 20, 30, 40, 50, 60, 70, 80
Sets of removing one staff nothing to declare if queue length =
5, 10, 15, 20, 25, 30, 35, 40
Figure 7-17 demonstrates that the waiting time at each lane is similar for the first
half of the experiments. This means a quarantine desk for declaring is opened when
200 Chapter 7: Application of Advanced Resource Management (ARM)
the number of passengers waiting is greater than 10, 20… 80 and other policies are
fixed with initial values for the first seven scenarios. From scenario 8 to 15, the
selected policy is that a declaration lane desk is closed when the number of passengers
waiting is greater than 40, 35… 5. What is interesting is the variability in queuing time
in the second half of the experiment when the number of passengers waiting at nothing
to declare lanes is greater 80 for opening a desk and 10 for closing the counter. The
best policy to enhance quarantine performance was scenario 51 with (i) the opening
and closing of declaration system desks at queue thresholds of 10 and 40, respectively,
(ii) opening and closing counters of nothing to declare at thresholds of 20 and 5,
respectively. The maximum/average waiting time in the declaration system were 60.31
minutes and 13.17 minutes, respectively. The maximum/average waiting time for
nothing to declare was 27.86 minutes and 5.46 minutes, respectively.
Figure 7- 17: Quarantine facility results of adding and removing staffing policies
7.4.2 Sharing staff policy for integrated processes
This section discusses the results of the simulation referring to staff sharing
policies. Immigration is discussed first as it is located on both inbound and outbound
sides. Staff is shared between the two sides with priority for outbound immigration.
The criteria for selecting the best policy is based on Kirk (2013); the author found that
the average time spent in the immigration domain is between 6 and 7 minutes. Thus,
any policy within this range or lower is acceptable. From Figure 7-18, it is clear to see
Chapter 7: Application of Advanced Resource Management (ARM) 201
that the average waiting time for outbound immigration is in the range of 5 to 7
minutes for the first 16 scenarios and the last nine scenarios. The policies that can
provide acceptable waiting times are summarised in Table 7-2.
Figure 7- 18: Outcomes of simulated immigration staff sharing rules
Results suggest that to decrease the number of passengers waiting in the queue,
the number of staff hours is increased. Waiting times for both sides exhibit similar
behaviour for the first nine scenarios including maximum and average waiting time.
From scenario 11 there was a sharp rise in the maximum waiting time until it reached
peak waiting time in scenario 41. The swapping policy of scenario 41 is that if the
number of passengers waiting at outbound immigration is greater than 100 and the
number of passengers waiting at the inbound domain is less than 50, staff are moved
from inbound to outbound processing. If the number of outbound passengers is 20 or
less, staff are moved from outbound to inbound processing.
202 Chapter 7: Application of Advanced Resource Management (ARM)
Table 7 - 2: Summary of eligible sharing polices
The second integrated process is security screening since it is located in both
outbound and inbound process flows. The same policies applied to the immigration
domain have been used for this investigation. The results are compared with the actual
data collected by Kirk (2013) to understand the acceptable queue time at security.
Based on Kirk (2013), the average queue time at security screening was 3.75 minutes
while the maximum queue time was 17.09 minutes. Hence, the best rules will be those
where the resulting wait times are equal to or lower than these limits.
Figure 7-19 demonstrates the outcomes of the simulation considering the
performance metrics of the 65 sharing policies including the maximum/average
waiting time of inbound and outbound security screening and total staffing hours. The
maximum/average waiting time of outbound security in the first nine scenarios and in
the last 19 scenarios are acceptable policies. The average waiting time ranged between
3.80 to 4.86 minutes in the first nine scenarios and between 3.71 to 5.7 minutes, in the
last 19 scenarios. The maximum waiting time ranged between 17.18 to 22.89 minutes
for the first nine scenarios and between 16.47 to 30 minutes for the last 19 scenarios.
Scenario 5 is considered the best policy since it provides the minimum waiting time
for both outbound and inbound security with average waiting times of 3.92 minutes
and 6.70 minutes, respectively, and maximum waiting times of 17.18 minutes and
25.42 minutes, respectively.
Chapter 7: Application of Advanced Resource Management (ARM) 203
Figure 7- 19: Security screening facility results of sharing staffing policies
7.5 CHAPTER SUMMARY
This chapter demonstrated numerical experiments involving the ARM technique
for outbound and inbound flow processes. The first experiment is the base case
scenario which allocated staff based on static allocation methods. It clearly showed
how the static allocation method can provide less queueing times for all terminal
processes only if the upper limit of employees is chosen. However, the static allocation
method has a significant impact on operating time which caused long staffing hours.
The second experiment represented a scenario of allocating airport employees
dynamically. The model provides better outcomes in representing flows of both
passenger types departing and arriving service processes. It also able to manage the
operations significantly better by allocating the staff if needed which balanced queuing
time and operating hours of staff. The results of this investigation show that the total
staffing hours is halved or sometimes 65% lower using the dynamic allocation
approach. Dynamic allocation also has less variation than the static method and the
variance values are close and occur around the mean. Experimental results
demonstrated that dynamic allocation method can be significantly influenced by queue
threshold values in regard to adding/removing staff and sharing staff between
integrated processes.
Chapter 8 will discuss the development of an analytical optimization framework
for capacity planning. The opportunity of integrating the simulation model within an
analytical optimization framework is also demonstrated to decrease the cost of time
spent in the queues of the airport terminal.
204 Chapter 8: An Analytical Optimization Framework
Chapter 8: An Analytical Optimization
Framework
In recent years, airports have faced many challenges such as the continuing
growth in passengers which imposes significant strains on the air travel global
infrastructure that is expected to keep up with the increasing passenger flows. This
includes the capacity of the airports and their ability to process the increasing numbers
of passengers with high efficiency and minimum delay. At the same time, the required
expansion of the airport capacity might be limited by the available resources (e.g.
limited available land), environmental impacts and lengthy approval processes
(Barnhart et al., 2012). In addition, extension of the major airport infrastructure is
typically time-consuming and costly, which raises the need for the development of
smart systems and methods to improve airport performance within the available
infrastructure limitations.
Additionally, the airport terminal is a complex system and stochastic in nature
since it involves multiple stakeholders each performing a different facility in terminal.
It also has many interactions between different actors (Wu & Mengersen, 2013). Due
to the complex structure of airport terminals, the development of an analytical
optimization framework for studying passengers flow in airports under uncertainty of
future demand is difficult. These difficulties and challenges have led to studies of
overall terminal capacity planning problems. Previous studies have generally focused
on one element of the terminal or have not accounted for expandability (Solak et al.,
2009).
Therefore, the purpose of this chapter is to propose a mathematical approach for
capacity planning. This model will determine the expansion capacities for different
processes of the airport terminal. The objective function of the proposed model is to
minimise the cost of used resources and the total waiting time. A number of technical
constraints exist.
Chapter 8: An Analytical Optimization Framework 205
8.1 PROBLEM DESCRIPTION AND FORMULATION
This section describes and defines the variables and parameters used to
formulate the mathematical problem. The purpose of this model is to determine where
additional resources should be placed in order to reduce the cost incurred in wait times.
Different resource types cost different amounts, so the objective is also to acquire
resources at minimum total cost.
8.1.1 Model notation
Indices
𝑝, 𝑟, 𝑘, 𝑡, f process, resource, passenger types, period, shift
Sets
𝑃, 𝑅, 𝐾, 𝑇, F processes, resources, passenger types, periods, Shifts
Parameter
𝑁𝑟,𝑝, 𝑁𝑟 Maximum number of resources of type 𝑟 in process 𝑝 and across all processes
𝐶𝑟,𝑝 Cost of providing a resource of type 𝑟 in process 𝑝
𝑉𝑘,𝑝 Cost incurred per unit of waiting time for passengers of type k in process 𝑝
𝐵 Total budget available for capacity expansion
𝑛𝑟,𝑝 Current number of resources of type r in process p
𝜏𝑘,𝑝 Expected time taken to serve passenger 𝑘 in process 𝑝
Decision variable:
𝑁𝑟,𝑝 Number of additional resources of type r in process 𝑝
𝑊𝑘,𝑝 Total waiting time incurred in process 𝑝 for passengers of type 𝑘
The model is as follows:
Minimize ∑ ∑ 𝐶𝑟,𝑝𝑁𝑟,𝑝 𝑟𝑝 + ∑ 𝑘 ∑ 𝑉𝑘,𝑝 𝑊𝑘,𝑝𝑝 [Resource cost + cost of waiting] (1)
Subject to:
𝑁𝑟,𝑝 ≤ 𝑁𝑟,𝑝 ∀𝑝 ∈ 𝑃; ∀𝑟 ∈ 𝑅 [Upper bound] (2)
∑ 𝑁𝑟,𝑝 ≤ 𝑁𝑟 ∀𝑟 ∈ 𝑅𝑝 [Upper bound] (3)
∑ 𝐶𝑟,𝑝𝑁𝑟,𝑝 ≤ 𝐵 ∀𝑝 ∈ 𝑃 𝑟 [Budget constraints] (4)
𝑁𝑟,𝑝 ≥ 0 ∀𝑝 ∈ 𝑃; ∀𝑟 ∈ 𝑅 [Positivity] (5)
(𝑊𝑘,𝑝) = 𝑆𝐼𝑀𝑈𝐿𝐴𝑇𝐸(𝑁𝑟,𝑝, 𝜏𝑘,𝑝) [Calculation of waiting time via simulation] (6)
The objective function (1) has two components: (i) the cost of
purchasing/acquiring additional resources of type r in process p, and (ii) the total
passenger waiting time converted to a dollar value. Constraints (2) and (3) ensure that
the additional resources of type r do not exceed the maximum number of resources.
206 Chapter 8: An Analytical Optimization Framework
Constraint (4) restricts spending to a particular budget. Constraint (5) restricts the
decision variable 𝑁𝑟,𝑝 to be positive.
8.2 SIMULATED ANNEALING
To solve this model a meta-heuristic approach is advocated as constraint 6
cannot be handled using mixed integer programming without the application of a
simulation model. Of the different meta-heuristics, Simulated Annealing was chosen.
The simulating annealing (SA) algorithm is a meta-heuristic, effective and simple
optimisation algorithm used for the solving of probabilistic and non-linear
optimisation problems. As for any type of meta-heuristics algorithm, it is developed
by simulating and modelling one of nature’s phenomena (Burdett, 2015; Mohammadi
& Safa, 2016). The SA is coded in C++ and the simulation model is used for evaluation
purposes. Figure 8-1 demonstrates the details of the SA algorithm.
The role of simulated annealing is an iterative approach that is able to escape
local optima. This is done by starting with an initial solution and, during the iteration
loop, it moves to a neighbour solution. If the neighbour solution is better than the
current solution, the algorithm moves to it; otherwise the solution will be accepted as
the current solution with a probability 𝑃, which is presented as follows:
𝑃(𝑓) = 𝑒−1 ∗( ∆𝑓
𝑇 ) (7)
Where ∆𝑓 is the difference between the current solution and the neighbouring
solution of the objective function, and T is the temperature. At every temperature, a
selected number of perturbations are evaluated. SA requires several parameters (i.e.
primary temperature, the cooling rate, the number of function evaluations at every
temperature and the final temperature) to implement simulated annealing (Amaran,
Sahinidis, Sharda, & Bury, 2016).
At early stages the temperature is high and many non-improving moves are
accepted. SA time goes by, solutions are only accepted if a strict improvement occurs.
With the slow reduction in temperature, the worst solutions have less probability to be
accepted. According to Amaran et al. (2016), who stated that because of the
exponential form, the acceptance of neighbourhood points is more likely at high
temperature, there is lower probability as temperature is decreased.
Chapter 8: An Analytical Optimization Framework 207
Figure 8- 1: General steps of the simulated annealing
Therefore, several series of preliminary experiments were conducted to
determine an appropriate starting temperature. It is evident from the graph below, that
the best parameter values of this problem are as follows: temperature (T) = 15000,
cooling rate (α) = 0.015, and total number of iterations = 600.
Set initial parameters
Generate initial sequence
Generate Neighborhood
Stop
Adjust temperature
Stopping criteria
StoppingCriteria
Is it accepted?
No
Set the solution as the best
Assess New Solution
No
No
Yes
Yes
Yes
208 Chapter 8: An Analytical Optimization Framework
T =150 T =1500 T =15000
α =
0.1
5
α=
0.0
15
α=
0.0
015
Figure 8- 2: Selecting the best initial parameters
Chapter 8: An Analytical Optimization Framework 209
8.2.1 Simulated annealing algorithm description
Phase 1: Create initial solution
SA may be initialised with a randomly created soliton or via some
heuristic/constructive algorithm. However, because of the resource limitation
constraints, some of the generated solutions will not be feasible. These solutions
should be corrected via a corrective algorithm. Algorthim 1 is used to initialise a set
of solutions.
Algorithm 1: Create initial population
1 For (each Shift);
2 For (each process 𝑝1);
3 do
4 𝑁𝑟1,1,𝑝1
= random number between (1, Maximum number of resources of type 𝑟1,1
available in process 𝑝1 );
5 𝑁𝑟1,2,𝑝1
= random number between (1, Maximum number of resources of type 𝑟1,2
available in process 𝑝1 );
6 X = 𝑁𝑟1,1,𝑝1+ 𝑁𝑟1,2,𝑝1
; economy and business counters;
while x ≤ number of available 𝑟1,1 𝑎𝑛𝑑 𝑟1,2 ;
7 End
8 𝑁𝑟2,𝑝2 = Uniform (1, 𝑁𝑟2,𝑝2);
9 𝑁𝑟𝑛,𝑝𝑛 = Uniform (1, 𝑁𝑟𝑛,𝑝𝑛);
10 End
Phase 2: Create new solutions
In general, creating a new solution depends on local search improvement
algorithms and the control strategy or general optimisation. In this case, creating a new
solution can be done by randomly changing the number of resources for one process
selected randomly from the current solution. The creating of the new solution
algorithm is illustrated in algorithm 2.
Algorithm 2: Creating new solution 1 If (change in process 𝑝1);
3 while x ≤ number of available 𝑟1,1 𝑎𝑛𝑑 𝑟1,2 do
4 𝑁𝑟1,1,𝑝1
= random number between (1, Maximum number of resources of type 𝑟1,1
available in process 𝑝1 );
5 𝑁𝑟1,2,𝑝1
= random number between (1, Maximum number of resources of type 𝑟1,2
available in process 𝑝1 );
6 X = 𝑁𝑟1,1,𝑝1+ 𝑁𝑟1,2,𝑝1
;
7 Else If (change in 𝑝2)
8 𝑁𝑟2,𝑝2 = Uniform (1, 𝑁𝑟2,𝑝2);
9 Else
10 𝑁𝑟𝑛,𝑝𝑛 = Uniform (1, 𝑁𝑟𝑛,𝑝𝑛);;
210 Chapter 8: An Analytical Optimization Framework
Phase 3: Assess New Solution
In this step, the goodness of the new solution is evaluated. Algorithm 3
demonstrates the assessment procedures. The generated solution will be simulated to
measure a performance matrix, such as the average waiting time at each processing
point. Also, the best cost will be selected by comparing it with the current cost. The
acceptance probability for calculating the function of simulated annealing is:
SA_ Probability= 𝑒−1 ∗(
𝐶𝑜𝑠𝑡𝑛𝑒𝑤−𝐶𝑜𝑠𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑇𝑒𝑚𝑝𝑟𝑒𝑡𝑢𝑟𝑒
)
Algorithm 3: Assess New Solution 1 Function Local Search();
2 New cost = simulation ();
3 If (New cost < Best cost);
4 Update Best solution;
5 Best cost = New cost;
6 Else
7 If (New cost < Current cost);
8 Update Current solution;
9 Current cost = New cost;
10 Else
11 P=Random number between (0 , 1)
12 SA_ Probability= 𝑒−1 ∗(
𝐶𝑜𝑠𝑡𝑛𝑒𝑤−𝐶𝑜𝑠𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑇𝑒𝑚𝑝𝑟𝑒𝑡𝑢𝑟𝑒
)
13 If(P> SA_ Probability)
14 Reject the new solution;
15 Else
16 Accept the new solution
Phase 4: Stop criteria
Finally, the condition of stopping the SA algorithm is based on the given
maximum number of iterations. Algorithm 4 is an illustration of the main loop of the
stopping criteria algorithm.
Algorithm 4: main loop
1 Parameter initialisation
2 Function Create initial population()
3 Simulation ();
4 do
5 Function refinement ();
6 Simulation ();
7 Function Assess New Solution();
8 x++;
9 while (x< maximum number of iterations)
Chapter 8: An Analytical Optimization Framework 211
8.3 NUMERICAL TESTING AND ANALYSIS
In this section, the integrated meta-heuristic simulation, SA algorithm and the
simulation model were tested. The solution chromosome should simultaneously reflect
two main characteristics:
Number of resources for each process, such as check-in resources (i.e.
economy, business counters), security screening resources and immigration
resources (i.e. common counters, SmartGates)
The number of assignment resources for each shift.
In this numerical investigation, there are three types of process and five types of
resources that were considered. Process type one, the check-in process, has five
separate lines, each with eight counters, two for business and six for economy. Process
type two is security screening with five lanes. Process type three is immigration, which
has eight common counters and 10 SmartGates. It is assumed that there are three
periods during the day to which these processes are assigned to be operated. Figure 8-
3 is a snapshot of the simulation outputs. This work showed that how simulation model
can be used within analytical optimisation framework to expand the capacity of the
airport terminal.
Figure 8- 3: snapshot of simulated annealing results
The cost of waiting time is considered based on the given policy of acceptable
queue time in a particular process. This is named the cost of inconvenience, as it
212 Chapter 8: An Analytical Optimization Framework
exceeds given acceptable average waiting time. For example, passengers at the check-
in process can be classified as business and economy, each with different queue time
limits. Kazda and Caves (2015) argued that the average waiting time should not be
higher than 12 minutes for economy class and 3 minutes for business. The summary
of input data used in this study is listed in Table 8-1.
Table 8- 1: Summary of the input data
Domain of the airport Values
Check-in:
Cost for opening new check-in counter is
The acceptable average waiting time for Economy passengers is
Cost for inconvenience at check-in for economy is
The acceptable average waiting time for Business passengers is
Cost for inconvenience at check-in for Business is
20$
12 minutes
15$
3 minutes
25$
Security screening:
Cost for opening new security screening desk is
The acceptable average time that normal passengers should wait is
Cost for inconvenience at Security screening for normal passengers is
The acceptable average time that Diplomatic passengers should wait is
Cost for inconvenience at Security screening for Diplomatic passengers is
15$
5 minutes
15$
2 minutes
20$
Immigration:
Cost for opening new Immigration desk is
Cost for opening new SmartGate is
The acceptable average waiting time at common counter is
Cost for inconvenience at common counters is
The acceptable average waiting time at SmartGate is
Cost for inconvenience at SmartGate is
15$
10$
7 minutes
20 $
0.5 minute
10$
Two different methods of creating new solutions were used to generate a starting
solution. The first is creating a new solution randomly and the second is local search.
The random search method initialises SA with a randomly created solution, while the
local search initialises SA via constrictive algorithm by changing one solution
chromosome and then refined by SA. For each method, 10 runs were repeated with the
same parameters. The results of the runs are presented in Table 8-2 and Table 8-3. The
general parameters used for both methods are: temperature (T) = 15000, cooling rate
(α) = 0.015, and the maximum number runs is 1500.
Chapter 8: An Analytical Optimization Framework 213
The first column refers to the number of better solutions, where the average
number of better solutions obtained by random search approach is 7.9 and the local
search approach is able to find 7.8 better solutions on average. Column two presents
the total average waiting time spent in the airport terminal process. It is evident that
local search provides lower waiting times compared with the random approach. It
could reduce the total average waiting time by 44.05%. It also has shorter run time,
with an average of 15.27 minutes compared to 21.05 minutes for the random approach.
However, from the results presented in both tables, it can be clearly seen that the
random search method reduces the objective function value by 12.11%. The mean 𝜇
value of the objective function obtained from the random approach is $1998.3 whereas,
𝜇 of objective function value of the local search is $2256.
Table 8- 2: summary of simulated annealing results using random search technique
Random search
Run # # of better solutions
Total Ave waiting time
Objective function value
Run time (min)
Run1 6 118.73 2031 21.05
Run2 5 58.44 2001 21.16
Run3 10 126.39 2060 23.48
Run4 9 39.57 2038 23.43
Run5 6 64.74 1973 20.24
Run6 9 148.18 1874 20.55
Run7 8 112.714 1913 17.36
Run8 10 86.67 1997 21.55
Run9 7 127.8 2052 17.36
Run10 9 131.25 2044 24.36
𝝁 7.9 101.4484 1998.3 21.054
𝝈 1.7 34.725498 58.99160957 2.256671
214 Chapter 8: An Analytical Optimization Framework
Table 8- 3: summary of simulated annealing results using local search technique
Local search
Run # # of better solutions
Total waiting time
Objective function value
Run time (min)
Run1 7 38.88 2336 13.7
Run2 10 92.31 2195 14.02
Run3 9 80.49 2182 15.4
Run4 10 94.97 2042 15.48
Run5 8 22.25 2180 17.22
Run6 8 28.89 2250 22.22
Run7 8 72.6 1994 12.53
Run8 8 85.47 2531 17.27
Run9 4 86.45 2638 12.21
Run10 6 45.6 2215 13.27
𝝁 7.8 64.82 2256.3 15.27
𝝈 1.72 26.45 186.49 2.88
From the 10 replications of both random search and local search, solution
numbers 4 and 6 from the random search and local search were selected as the best
solutions, for two reasons. The first reason is that the value of the objective function
is closer to the mean value of all the objective values. The second reason is that the
chosen simulation runs provide the minimum total average waiting time in the airport.
Figure 8-4 demonstrates the best solution given by the random search for the
solution run number 4. The optimal solution for this simulation run in regards to
opening additional resources for check-in is summarised in Table 8-4. The average
waiting time is 1.91 minutes for business class and 34.5 minutes for economy class
passengers.
Table 8- 4: Check-in additional resource results using the random technique
Line 1 Line 2 Line 3 Line 4 Line 5
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Shift 1 1 4 2 3 2 4 1 4 1 4
Shift 2 2 2 1 1 1 1 1 2 2 4
Shift 3 2 4 1 6 1 6 1 2 2 4
Chapter 8: An Analytical Optimization Framework 215
For the security screening checkpoints, the opening resources are 5, 2 and 3
control checkpoints for shift 1, shift 2 and shift 3, respectively, having an average
waiting time of 3.04 minutes. The opening common immigration process is 4, 3 and 5
counters, while for SmartGates there are 8, 9 and 7 kiosks for the three shifts, having
average waiting times of 0.11 and 0.02 minutes for the common immigration desks
and SmartGate kiosk, respectively. The total cost of opening all resources is $2038
given the total average time spent in the queues is 39.57 minutes.
Iteration
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
obje
ctiv
e fu
nctio
n va
lues
0
10000
20000
30000
40000
50000
Bes
t so
lutio
n
0
2000
4000
6000
8000
Objective
Best solution
Results of simulation run 4 of random approach
Iteration
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
Tota
l Ave
wai
ting
time(
min
)
0
500
1000
1500
2000
Bes
t sol
utio
n
0
100
200
300
400
500
waiting_time for all runs
Best solution
Figure 8- 4: SA optimisation results using the random method of creating new solutions
Figure 8-5 illustrates the optimal solution provided by the SA algorithm using
local search for creating a new solution. In this simulation run, the solution is
characterised from the total cost of $2250 given the total average waiting time spent
in the system is 28.89 minutes. The optimal solution for this simulation run is opening
a check-in resource based on the detailed information listed in Table 8-5. By adding
216 Chapter 8: An Analytical Optimization Framework
this resource, the average waiting time at the check-in process will be 1.56 minutes for
business class passengers and 7.03 minutes for economy, a reduction of 20.17% for
business and 132.29% for economy compared with the random search method.
Table 8- 5: Check-in additional resource results using local technique
Line 1 Line 2 Line 3 Line 4 Line 5
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Bu
sin
ess
Eco
no
my
Shift 1 1 4 2 3 3 5 2 5 1 5
Shift 2 2 7 1 2 1 3 4 3 1 4
Shift 3 2 1 2 2 2 2 4 1 2 1
For the process of security screening checkpoints, the best solution can be found
if opening 4, 4, and 5 security control checkpoints for shift 1, shift 2 and shift 3
respectively. By opening these number of resources at the security screening process,
the average waiting time is 7.92 minutes. Finally, the immigration process should open
5, 4 and 3 common immigration counters and 10, 10 and 9 SmartGates for the three
shifts, in order to get the optimal solution, having the average waiting of 0.10 minutes
and 0.012 minutes for the common counters and SmartGates, respectively.
Chapter 8: An Analytical Optimization Framework 217
Iteration
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
obje
ctive
func
tion
value
s
0
2000
4000
6000
8000
10000
12000
Best
sol
utio
n
0
500
1000
1500
2000
2500
3000
Objective
Best solution
Results of simulation run 6 of local search approach
Iteration
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
Tota
l Ave
wai
ting
time(
min
)
0
500
1000
1500
2000
Best
sol
utio
n
0
20
40
60
80
100
120
waiting_time for all runs
Best solution
Figure 8- 5: AS optimisation results using the method of creating new solution using local technique
8.4 CHAPTER SUMMARY
This chapter discussed the development of a mathematical approach to capacity
planning. The objective of the model is determining where additional resources should
be located to decrease the cost of time spent in the queues of the airport terminal. Since
the proposed problem is probabilistic and non-linear, a meta-heuristic approach is
advocated because constraint 6 cannot be handled using mixed integer programming
without the application of simulation.
Two different approaches for creating new solutions were used in this study. The
first one is creating a new solution using the random technique and the one is creating
a new solution by using the local search technique. The random technique decreased
the objective function value by 12.11% however, the local search technique can reduce
the total average waiting time by 44.05%. It also has shorter run times, with averages
of 15.27 minutes compared to 21.05 minutes for the random approach.
218 Chapter 9: Conclusion
Chapter 9: Conclusion
9.1 INTRODUCTION
This chapter outlines the conclusions of this thesis. It first discusses the primary
outcomes and how they contribute to the body of knowledge of the field of passenger
flows modelling and airport operational planning. It then provides a summary of the
key research activities and answers the research questions presented in Chapter 1. It
finishes by making recommendations for airport operations management and the
directions of future research.
9.2 SUMMARY AND DISCUSSION
This research has resulted in the development of a holistic model based on the
combination of simulations, ARM algorithms and analytical optimisation approaches.
This model is a decision support tool for airport operators to make well-versed
decisions for efficient airport operation. Application of the model facilitates the
operational planning of integrated inbound and outbound flow processes and
determines the impacts on passenger flow and congestion.
Initially, relevant literature was reviewed to select suitable approaches. Then, the
major processes of outbound and inbound systems were mapped out based on the
business processes models developed by Mazhar (2015) for Australian international
airports. The second phase involved development of the simulation models for
outbound and inbound passenger flow processes using ExtendSim software. The
data/input requirements of the simulation model were categorised into three basic
categories:
Flight schedule such as departure time, boarding time, the size of the
aircraft, and the name of the airline.
Passenger characteristics, e.g. the percentage of passengers travelling by
business class, the percentage of passengers using SmartGates, etc.
Operational characteristics such as distribution of processing time, and
characteristics of the counters for processing facilities.
Chapter 9: Conclusion 219
The final phase was the development of an optimisation approach for capacity
planning. By applying the developed optimisation algorithms, optimum resource
allocation was obtained. The purpose of the capacity planning model is to determine
where additional resources should be placed to reduce the cost of waiting as well the
obtained resources. The key research activities are summarised below.
Chapter 2 reviewed the existing research on recent issues related to passenger
flow, security and staff allocation in a complex environment such as airport terminals.
It then reviewed models used to resolve such problems. These models can be classified
as either ‘analytical’, ‘simulation’, or ‘hybrid’ models, providing decision support
capabilities at all levels of detail: from macroscopic, through mesoscopic, to
microscopic. The literature review highlighted the improvements made in our
understanding of passenger flow modelling to date. Despite the extensive research on
airport issues, the literature suggests that an aggregate model, providing integrated
views associated with the performance of processing points and facilitating both
outbound and inbound processes, is still needed. From the state-of-the-art and state of
practice reviewed, suitable methods for addressing the primary aim of the research
were selected. The simulation method represented by DES was the best technique for
this research when associated with modelling of the system operations and could
capture system behaviour at the macroscopic level (Furian, Neubacher, Vössner,
O’Sullivan, & Walker, 2014; Siebers, Macal, Garnett, Buxton, & Pidd, 2010). The
DES was selected as it can provide a true presentation of the system and can deal with
the analysis of passenger flow problems via a top-down modelling approach.
Moreover, the DES has the capability to model uncertainty and non-linearity
(Sachidananda et al., 2016).
In Chapter 3, the conceptual framework of a holistic simulation model for an
international terminal was introduced. The proposed framework consists of three
major modules including a simulation model for outbound processes, a simulation
model for inbound processes and algorithms for overall management of airport
resources. Due the complexity of the holistic model, Chapter 3 developed the first
phase as a simulation model for departure passenger flows. This phase focused on
modelling outbound standard terminal operations including check-in counters, security
screening, immigration and custom and boarding. The model was built around
220 Chapter 9: Conclusion
hierarchy modules to provide insights into the interactions between processes and sub-
processes.
The developed framework for outbound passenger flow was illustrated by a
series of experiments in Chapter 4. Different arrival patterns of passengers were
implemented in outbound process simulations. The simulation outcomes provided a
better understanding of the behaviour of passenger airport access which could lead to
reduced waiting time and possible congestion by increasing the number of working
stations (i.e. number of check-in counters) at peak times.
Chapter 5 presents the integrated simulation model by combining a complete
inbound simulation model and advanced resource management algorithms to describe
the interaction between passenger behaviour and outbound/inbound processes. The
inbound simulation model was concerned with passenger disembarking including
generating inbound passenger attributes (i.e. SmartGate users, walking speed, number
of bags, etc.), inbound security screening, immigration, baggage claim and quarantine.
We developed an algorithm to generate the attributes of passengers based on data taken
from flight scheduling, i.e. arrival time of flight, gate number and capacity of flight.
The significant contribution of this chapter is the development of advanced resource
management (ARM) algorithms that can dynamically allocate/reallocate or add and
remove airport personnel based on given queue threshold policies. In ARM, the
processes were categorised into two categories:
Non-integrated processes including check-in and quarantine
Integrated processes such as security screening and immigration processes.
Chapter 6 demonstrates the capability of the developed simulation model to
accurately reflect an actual airport terminal. The validation processes were confirmed
by comparing simulation results with the real data provided by King Khalid
International Airport (KKIA). Four types of simulation outcomes, including the
average and maximum waiting time in the queue and the average and maximum cycle
time at each departure facility, were considered. Results showed that the developed
model is more applicable for Australian airports such as Brisbane International Airport
than an external airport, such as KKIA. Since each airport proposes different
operational processes, such as security (Akgun, Kandakoglu, and Ozok (2010), the
model results can be further improved and have more accurate validation as follows:
Chapter 9: Conclusion 221
By collecting data using the video camera technology. This technology can
provide samples and large volumes of samples more efficiently and
accurately, because they often record a full day, week or even a month of
data
By changing the input requirements and running ‘what-if’ scenarios; since
airports are exposed to external effects and the developments of air traffic
Chapter 7 provided an integrated view of all the airport terminal processes using
the devised ARM strategies for allocating and reallocating terminal staff. This chapter
compared the static allocation and developed dynamic allocation methods in terms of
the total average waiting time and total staffing hours. Based on the given assumption
of allocating the staff if needed, the developed ARM model decreases the total staffing
hours is some terminal facilities, such as check-in, by up to half compared with Static
Allocation Base Case method results. It also balanced the averaging waiting time and
operation hours since the staff is only allocated if needed. The ARM can be a decision
support tool and efficiently used to support and model real-world airport staff
allocation planning problems.
Chapter 8 discussed how the physical resources of airport terminals can be
optimised. An analytical model was developed and integrated with the simulation
model to determine where the additional resources should be placed to minimise
waiting time costs. This model also aimed to obtain resources at minimum total cost.
9.3 RESEARCH CONTRIBUTIONS
The motivation for this work was to develop a model capable of studying
passenger flows and staffing requirements at airport international terminals as a single
unit by facilitating the integration of outbound and inbound systems. Previous research
was limited by the focus on individual processes or fragmented areas of decision
making procedures of airports. This limited focus produced a knowledge gap in
relation to the terminal system as a whole and how the processes in different sectors
influenced the operation and management of the entire system.
The primary contribution of this thesis is the development of a holistic model
that integrates the major inbound and outbound flow types to analyse passenger flow
issues in an international airport. By facilitating and integrating inbound and outbound
processes, an integrated view of overall airport operations can be achieved (Yamada
222 Chapter 9: Conclusion
et al., 2017; Zografos et al., 2013). The following sub-sections briefly outline the
significant contributions of this thesis to the body of knowledge on passenger flow
modelling within airports.
9.3.1 Framework for airport outbound passenger flow modelling
Based on objective one, a framework for outbound passenger flow modelling
was developed using DES and built using ExtendSim V9.2 simulator software (see
section 3.5). Excel macro visual basic programming was used to model and generate
model inputs including information related to flight schedules, passenger
characteristics and boarding characteristics. The most important feature of this model
is its ability to predict the effect of different flight schedules which can be used as a
feedback mechanism for improvements before implementation. Results show that
flight schedules have a large impact on passenger flows. Integrated flight schedule
creation and passenger simulation analysis may help address some of the issues of
passenger flow within airport terminals, especially for the two most-
affected processes: security screening and immigration.
9.3.2 Investigating the effect of arrival patterns of departing passengers on the
departure terminal operations
As discussed in objective two, a model for passenger arrival procedures was
developed using MATLAB to estimate the volume of travellers arriving at the airport
over time with respect to different types of distribution functions. The model can
obtain the total number of passengers per time interval for all flights. This model was
integrated with the outbound simulation model to study the impacts of different arrival
patterns on departure flow processes.
Simulation results suggested that different arrival patterns can significantly
affect the performance of operational processes in the airport terminal taking into
account the maximum/average number of passengers waiting in the queues and the
maximum/average waiting time in front of working stations. The policy of the time to
open check-in counters under a given mean value has a significant impact on the
departing passenger arrival profile, especially when the policy of time to show up at
the airport equalling the mean value µ is applied. As a result, potential congestion and
longer waiting times might occur in airport processing activities including check-in,
security and immigration. This can lead to significant congestions and delays which
could lead to passengers missing flights and poor passenger experience. The passenger
Chapter 9: Conclusion 223
arrival distribution patterns are a major factor in planning airport-terminal facilities,
such as the number of check-in counters and service agents, along with the operation
times of passenger check-in and queue length (Fayez, Kaylani, Cope, Rychlik, &
Mollaghasemi, 2008; Park & Ahn, 2003). It can also affect the performance of entire
outbound terminal operations and other related services. For example, based on the
results of Chapter 4, the best time to arrive at the international airport is 4 hours before
the flight (given the mean value before flights is 60 minutes) which could lead to
shorter queues and waiting times at main outbound processes and more time for
discretionary activities and retail which is desirable for airport retail operators.
9.3.3 Advanced resource management strategies
As mentioned in objective three, the developed ARM algorithms were integrated
with the simulation model (see section 5.4) and used to manage operations by
allocating staff if needed, leading to a balance between acceptable waiting times in
queues and staff operating hours. Our ARM approach is significant because it can be
used in other domains to manage resource allocation with slight modifications.
Compared with the SABC results, dynamic allocation could halve the total staffing
hours in some processes, such as check-in up, since the staff will be allocated if needed.
Additionally, the dynamic allocation method can be influenced by queue threshold
values in regard to adding/removing staff and sharing staff between integrated
processes. This approach can also be used to reduce the number of delayed flights and
the total operating hours as explained in section 7.3.2. The developed approach is
significant because it can be used to manage entire airport systems and provide better
results than existing approaches. For example, the results obtained from our model
demonstrated better improvement than Kierzkowski and Kisiel (2016), especially in
regard to the total average waiting time and staffing hours of security screening as
summarised in Table 9-1.
224 Chapter 9: Conclusion
Table 9- 1: Comparisons of developed ARM results with Kierzkowski and Kisiel (2016)
Compared
study
Total average waiting time (min) Total staffing hours (hours)
ARM model (Artur Kierzkowski &
Kisiel, 2016)
ARM model (Artur Kierzkowski
& Kisiel, 2016)
Static case 9.25 9.538 96 285
Dynamic case 7.14 7 65.23 162
9.3.4 Development of a novel holistic model for facilitating outbound and
inbound processes
As discussed on objective four, further expansion of the simulation model was
done using ExtendSim to facilitate the integration between inbound and outbound
processes. The model can be run with either elements or both flow processes
simultaneously. Each element has its own input data of departing and arriving flights
and outgoing and incoming passenger attributes (see section 3.4 and section 5.2.2).
Further interaction between inbound and outbound processes can be investigated by
using the ARM approach within the developed simulation to improve passenger flows.
The proposed model also provides flexibility in changing the order of the
processes as each airport is operated differently. For example, in Australia, the security
screening process of Brisbane International Airport is located before immigration,
while in Perth, security comes after immigration (Mazhar, 2015; Shuchi, 2016).
Furthermore, the model can be changed and modified at the operational level to
characterise passenger flow with respect to a set of parameters involving flight
schedules, processing time, and passenger characteristics (i.e. business and economy,
declaration lane the passenger can use). Statistics for aggregate performance matrices
of outbound and inbound facilities can then be collected.
9.3.5 Strategic and operational planning techniques
As stated in objective five, the last thesis contribution takes the form of strategic
planning. Most existing research has considered the strategic level (Solak et al., 2009;
Sun & Schonfeld, 2015), while other research has focused on the operational planning
level only (Fayez et al., 2008; Manataki & Zografos, 2009b; Schultz & Fricke, 2011).
The Capacity Planning Model (CPM) can provide strategic planning while the
proposed simulation model which can be used for the operational planning level. This
Chapter 9: Conclusion 225
thesis has contributed to the body of knowledge by enabling two levels of planning,
operational and strategic.
9.3.6 Practice contribution
The developed holistic model can immediately be of practical use in two
different ways. The first significant practical contribution is to evaluate the efficiency
and performance of airport operations. This would enable airport operations managers
to identify the potential bottlenecks. The model also supports what-if and trade-off
scenario planning for evaluating changes in operational policies. The model can be
used to deal with sudden problems or any unexpected congestion situations.
Additionally, the proposed simulation model can be used to investigate several related
factors that might affect the performance of the terminal operations. Examples of these
factors are new security regulations and new requirements, resource availability,
arrival and departure patterns, new technology, randomness and variability
characteristics throughout the system.
Moreover, the model outputs of the current research can be treated as a decision
support tool. The proposed model has the potential to improve the overall performance
and efficiency of terminal operations, but only if it is integrated into appropriate ARM
strategies. It can quantitatively forecast and compare the effect of new procedures and
counterpart regulations and decisions on the operational performance of terminal. For
example, decisions associated with passenger arrival time to the airport based on the
departure time of the scheduled flight and queue length threshold to open and close
checking counters or allocate and reallocate personnel.
9.4 LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
Through the course of this research, there were two major limitations observed.
First, there was a lack of access to the detailed data related to operational facilities.
This was due to the recent strict regulations associated with security issues set by
government. This limitation resulted in difficulties in developing, validating and
calibrating the modelled passengers’ flows within an international terminal. The
accuracy of the interaction between passengers and terminal operations and the
decision-making procedures affect the model’s reliability. Information, such as
processing time distribution, service rate and the acceptable waiting time in queues for
different terminal processes, also significantly affect system performance and
226 Chapter 9: Conclusion
passenger satisfaction. Due to the difficulties of accessing this data, the proposed
model has been simplified either by utilising available data collected by previous work
or by making experimental assumptions where needed.
The second limitation is that the proposed model in this thesis is developed for
a specific Australian international airport. This is an issue as most airports facilitate
their passengers in different ways (Fernandes & Pacheco, 2002). For example,
international airports commonly open check-in counters 3 hours prior to departure
while domestic airports open check-in counters just 90 minutes beforehand (Cheng,
2014; Schultz & Fricke, 2011). Therefore, passenger arrival distribution patterns can
vary between local airports and international airports. The simulation model of
passenger flows considers all aspects of international airports. Hence, it can be adapted
widely. Since domestic airports are considered a subset of international airports, the
proposed model can be utilised to simulate all scenarios that may occur in domestic
airports after adjustment of the processes in the model based on empirical data.
To address these limitations, the following future research directions are
recommended:
Determine the influence of arrival patterns on resource-allocation management
including both outbound and inbound systems of an international terminal. This
can be done by considering different distributions functions to determine the
primary inputs of departing passenger arrival profiles. This is likely to provide
significant new outcomes about the expected impacts of such inputs on the
effective allocation of resources. Ultimately, the efficiency of all airport terminal
operations can be improved.
Determine how to employ the developed models to larger airports with two or
more terminals. For example, Dubai International Airport has three separate
terminals—Terminal 3 alone has four concourses each with 26 gates including
five A380 gates and a total capacity of 60 million passengers. Similarly, further
research can be conducted to study the problem of passenger flow within airports
dealing with international and domestic passengers in the same terminal, such as
the Gold Coast Airport.
As previously mentioned, the holistic framework proposed in this thesis was
developed based on the available data (Airport of the Future Project undertaken
Chapter 9: Conclusion 227
by QUT) and available literature. The key weakness of this evaluation technique
is the lack of real-world data or scenarios. Therefore, further research is needed to
validate the developed model by comparing its results with the real-life scenarios
to increase model accuracy and robustness as well as meet practical requirements.
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Appendices 239
Appendices
Appendix A: Simulation Parameters
The flight schedule data for outbound in international Australian airport.
Table A- 1: Timetable of outbound flight used in the model
Airline Departure
time
Number of
PAX
Airline
Group
Departure
Gate Emirates 3.3 222 1 1
Qantas 6.2 134 2 2
Emirates 7.25 222 3 3
Air Vanuatu 8.2 134 4 4
Aircalin 8.2 134 5 5
Cathay Pacific 8.3 134 1 6
China Airlines 9 134 2 7
China Southern
Airlines
9 128 3 8
Etihad 9.1 241 4 9
Jetstar 9.15 134 5 10
Air Canada 9.3 120 1 11
Air New Zealand 9.3 51 2 12
Air Niugini 9.35 241 3 13
Air Vanuatu 10.3 134 4 14
Aircalin 10.3 134 5 15
Cathay Pacific 10.4 180 1 16
China Airlines 11.15 222 2 17
China Southern Airlines
11.2 251 3 18
Emirates 12.3 258 4 19
Etihad 13.4 258 5 1
Fiji Airways 14.2 258 1 2
Hawaiian Airlines
14.4 265 2 3
Jetstar 16.45 134 3 4
Korean Air 17.4 99 4 5
Nauru Airlines 17.4 134 5 6
Philippine
Airlines
17.45 134 1 7
Qantas 18 134 2 8
Singapore
Airlines
18.3 134 3 9
Solomon Airlines 18.35 180 4 10
Thai International 20.45 222 5 11
Virgin Australia 21.2 134 1 12
Singapore
Airlines
22.4 134 2 13
Solomon Airlines 22.45 243 3 14
Thai International 23.45 265 4 15
Virgin Australia 23.5 265 5 16
240 Appendices
The flight schedule data for inbound international Australian airport.
Table A- 2: Timetable of inbound flight used in the model
Flight # Arrival time Flight capacity Arrival Gate VA46 5:45 176 1 QF16 6:10 353 2 QF52 6:15 297 3 QF52 6:15 239 4 EK434 6:25 489 5 QF62 6:45 297 6 KE123 6:50 276 7 QF98 6:50 297 8 VA8 6:50 361 9
SQ235 7:05 285 10 AC35 7:15 251 11
QF124 7:15 168 12 QF68 7:30 297 13
VA153 7:35 176 14 VA103 8:00 176 15 QF124 8:15 168 16 CZ381 8:25 284 17 VA153 8:35 176 18 VA127 8:45 176 19
PX3 9:25 188 1 CX103 9:50 251 2
VA119Z 10:05 176 3 NZ135 10:05 332 4 FJ921 10:30 118 5 SQ255 10:35 285 6 CI53 10:45 313 7
PR221 11:00 368 8 NZ135 11:05 332 9 TG473 11:50 264 10
JQ6 13:20 335 11 VA115 14:25 176 12 PE532 14:45 264 13 IE700 15:30 156 14 VA176 15:45 275 15 QF134 16:30 168 16 NZ805 16:30 168 17 NZ739 16:35 168 18
PX5 16:40 188 19 QF126 16:55 168 1 VA107 17:10 176 2 VA188 17:10 176 3 VA170 17:25 176 4 NZ805 17:30 168 5 NZ739 17:35 168 6 VA117 17:35 176 7 EY484 17:40 231 8 ON1 17:45 130 9
QF126 17:55 168 10 NF20 18:10 170 11 SB150 18:20 146 12 EK435 19:10 489 13 SQ245 19:30 285 14 HA443 19:45 294 15 NZ733 20:20 168 16 CI54 21:15 313 17
NZ733 21:20 168 18 CX157 22:15 251 19 EK432 22:30 354 1 VA161 22:50 176 2
Appendices 241
Appendix B:
Code of the development of Airport library and Advanced Resource
management (please see the link below):
https://www.dropbox.com/home/Airport%20code