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THESIS DEFENSE 

 Investigation of Agent-Based Approaches to Enhance

Container Terminal Operations

byOmor Sharif

Presented in Partial Fulfillment of the Requirements

For the Degree of Master of Science inCivil Engineering

2011

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What is a Container Terminal (CT)?

An interface between ocean and landShips are loaded and unloadedContainers are temporarily storedManage handling of Containers etc

Flow of Containers and Decision Problems

BERTH ALLOCATION

QUAY CRANE SCHEDULING

TRANSPORT OF CONTAINERS TO STORAGE AREA AND VICE VERSA

YARD OPERATIONS - STORAGE SPACE ASSIGNMENT

YARD OPERATIONS – YARD CRANE SCHEDULING

DELIVERY AND RECEIPT OPERATIONS (GATE OPERATIONS)

CUSTOMER/DEPOTS

TERMINALGATE

STORAGEAREA

SHORE/BERTH

VESSEL/ SHIP

XTs XTs AGVs/ITs/SCs QCsYCs

ARRIVAL AND STORAGE RETRIEVAL AND LOADINGRETRIEVAL AND PICKUP UNLOADING AND STORAGE

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Two Research Studies

Yard Crane Scheduling Problem

Truck Queuing at Terminal

Gates

Research Topics

1. Sharif, O., Huynh H. (2011) “Yard crane scheduling at seaport container terminals: A comparative study of centralized and decentralized approaches”. Paper to be submitted for publication.2. Sharif, O., Huynh, H., Vidal, J. (2011) “Application of El Farol model for managing marine terminal gate congestion”. Submitted to Journal of Research in Transportation Economics.

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Journal Article I 

 Yard Crane Scheduling at Seaport Container Terminals: A Comparative Study of

Centralized and Decentralized Approaches 

by

Omor Sharif and Nathan HuynhUniversity of South Carolina

Paper to be submitted for publication

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OutlineWhat is Yard Crane Scheduling Problem?

Review of Centralized Solution

Review of Decentralized Solution

Design of Experiments and Results

Comparative Performance between the two approaches

Conclusion/Future Directions

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Yard Crane Scheduling Problem

Objective: Determining best sequence of trucks to serve by each yard crane.

Challenges:There are fluctuations in truck arrivalJob locations are distributed throughout the yard zoneGood decisions are difficult to conceive manually

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Yard Crane Scheduling (YCS) Problem

Operational improvement of container terminal

Reducing drayage trucks turn time

Efficient allocation of scarce resources

Environmental Concerns

Motivation

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Solution to YCS Problem

Centralized Approaches

-OR Optimization- IP

- MIP

Decentralized Approaches

- Agent-based Modeling

YCS Problem Solution

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Research Questions

Comparative Study between the two approaches

Contrasting assumptions?

Strengths and weaknesses?

Relative performances?

Suitability for implementation?

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Centralized Approach

Based on the work of Ng (2005)

IP was developed for optimal crane scheduling

Considers multiple yard cranes and known arrival times

Excessive computational time required to solve IP

Dynamic programming based heuristic is proposed

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Centralized ApproachHow the Heuristic solves YCS?

Heuristic has TWO phases

First Phase (Find Best Partition) • Partitioning of the Yard Zone• Several smaller groups equal to number of

YCs• Job handling follows greedy heuristic• Output is best partition with least total

waiting

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Centralized ApproachHow the Heuristic solves YCS?

Heuristic has TWO phases

Second Phase (Job Reassignment)

• Job reassignment between adjacent YCs• Interference check required• Algorithm considers two cranes at some

time• Output is the minimum total waiting found

by heuristic

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Centralized ApproachA Sample Heuristic Solution

First PhaseSolution

Second PhaseSolution

Path of the Cranes

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Decentralized ApproachDistributed perspective in recent years

Based on the work of Huynh and Vidal (2010)

Agent based approach

Each YC is an agent seeking to maximize utility

Decisions are based on the valuation of utility function

Utility functions are designed to minimize waiting time

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Decentralized ApproachUtility Functions

Distance Based Utility

Time Based Utility

D = Distance to TruckT = Truck Wait Timep1 and p2 = Penalty Values (discouraging penalties)Xinterference, Xproximity, Xturn and Xheading are binary variables

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Decentralized Approach

Simulation model, coded in Netlogo Netlogo: A multi-agent programmable Environment

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Key DifferencesCentralized approach

Decentralized approach

Optimization strategy

Global optimization.

Agent based local optimization.

Work flow Optimal schedule.

Individual decisions.

Arrival information

Assumes complete information.

No assumption.

Truck sequencing

Greedy approach Cranes’ utility functions.

Implement-ation Dynamic

heuristics.Agent-based simulation.

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Experimental DesignA large set of YCS problems were solved

Experiment Set 1: Impact of Number of Yard CranesNumber of YCs ⟶ 2 to 4Experiment Set 2: Impact of Truck Arrival RateArrival Rate ⟶ 5, 10 and 15Experiment Set 3: Impact of Yard SizeNumber of Yard blocks ⟶ 1 to 3Experiment Set 4: Impact of Truck VolumeNumber of Jobs ⟶ 20, 50 and 80Job location distribution ⟶ Random Uniform DistributionJob arrival distribution ⟶ Poisson Distribution

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Comparative Performance between the two approachesOptimality - Minimize the truck waiting timeCentralized Approach• Heuristic produces near-optimal schedule• On average 7.3% above the lower bound

Decentralized Approach• No advance schedule for the agents• On average 16.5% above the heuristic

solution

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Comparative Performance between the two approachesOptimality - Minimize the truck waiting time

Fig: Mean Index for different truck arrival rates

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Comparative Performance between the two approachesOptimality - Minimize the truck waiting time Fig:

Mean Index for different yard sizesFig: Mean Index for different job volumes

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Comparative Performance between the two approachesScalability and computational efficiency

Centralized Approach• Highly sensitive to the size and complexity• Requires performing the computation in

advance

Decentralized Approach• No computation time required in advance• Disaggregated, handle large and complex

problems

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Comparative Performance between the two approachesAdaptability

Centralized Approach• Assumes complete information on supply

and demand• Requires rescheduling to adapt with changes

Decentralized Approach• No assumptions on the arrival-time of trucks• Monitor changes continuously, adapt rapidly

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Concluding Remarks/ Future Work

Two approaches have complimentary solution propertiesHybrid approaches may offer better resultsProposed Hybrid Approach I• Local optimization models for cranes• Coordination for best partition within yard

zoneProposed Hybrid Approach II• Solve global optimization periodically• Switch to adaptive agent-based model when

necessary

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Journal Article II 

 Application of El Farol Model for Managing Marine Terminal Gate

Congestion 

by

Omor Sharif , Nathan Huynh and Jose VidalUniversity of South Carolina

Submitted to Journal of Research in Transportation Economics

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OutlineGate Congestion problem at CT

Proposed Model and Implementation

Design of Experiments and Results

Concluding Remarks

Congestion Problem at Terminal Gates

Documentation processing, inspection, security checks etcLong waiting time due large number of idling trucksImpact turn around time of drayage trucksEnvironmental concern due to significant emission

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Attempted Solution

Appointment Systems/

Reservation Systems with Time

Windows

Real Time Gate Congestion

Information Using Webcams

Solution to the Gate Congestion Problem

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Proposed Agent-based Model

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Proposed Agent-based Model (Contd.) N ≡ Set of Depots (n ∈ N)T ≡ Set of Trucks (t ∈ T)L ≡ Tolerance (Max allowed waiting time)E (W) ≡ Expected waitSEND? (n, t) ≡ 1 if E (W) ≤ L 0 otherwiseTotal time before entry into port = T (n, P) + Q(t) + S(t)Wait at gate, W(t) = Q(t) + S(t)I ≡ Discretization intervalAverage waiting at xth interval, Historyx = { }

x

x,t

C

)x,t(W)x,t(W

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Proposed Agent-based Model (Contd.) Parameters related to ‘Predictors’S = [s1, s2 ,s3 ,..., sz] ≡ Predictor space containing z predictorsk ≡ Number of predictors chosen from Smy-predictors-list(n) ≡ Predictor set of depot agent nmy-predictors-scores(n) ≡ Rank of predictors of depot agent nmy-predictors-estimates(n) ≡ for each predictorsactive−predictor(n) ≡ Best performing predictor for depot agent n Updating of scoresOriginal Precision Approach:  is a number strictly between zero and one

Proposed Agent-based Model (Contd.)

Pseudo Code of the Program – Part of the Main Loop

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Model Implementation

Simulation model, coded in Netlogo

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Experimental DesignParameter Value UnitNumber of Depots 10 NosDispatch rate (θ) 12 trucks/

depot/hrMean transaction time (μ)

3, 4, 5, 6, 7 and 8

minutes

Tolerance (L) 15, 20, 25 and 30

minutes

Total predictors 200 NosPredictors per depot (k)

12 Nos / depot

Update interval (I) 5,10 and 15 minutesMaximum memory (m)

20 intervals

Predictor scoring policy

Original precision

n/a

Alpha 0.5 n/a

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Results (Mean wait and Total completion)Fig: Impact of tolerance on mean wait time of trucks

Fig: Impact of tolerance on total completion time.

Results (Mean wait time history)Fig: Mean wait time of trucks (I =15 minutes, L = 15 minutes)

Fig: Mean wait time of trucks (I =10 minutes, L = 10 minutes)

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Results (Base Case Comparison)

43% and 63% lower mean wait time for I = 5 and 10 mins22% and 40% lower maximum wait time for I = 5 and 10 mins18% and 40% higher completion time for I = 5 and 10 mins

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Concluding RemarksProposed model provides steady truck arrival

Adopt higher ‘I’ for distributing demand

Good amount of emission reduction over ‘do-nothing’

First study of its kind

Additional studies are required to understand complexity

More sophisticated learning models

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Thank You

Questions ?