Alzraiee 2014 Automation in Construction

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1. Introduction

down Structure packages into activities, and t. Such aning, exsions,projece compf the coprojeceffect4]. Inte

elements, whether internal among the elemen

hinder developing realistic and a representative planning models. The

the project's execution. In reality, the project is a system of interrelatedelements, in which each element is unique in nature, and interacts with

overcome by a more integrative computation environment, possibly

e activities. While de-t any surrounding fac-ure, rework cycle has

Automation in Construction xxx (2014) xxxxxx

AUTCON-01810; No of Pages 17

Contents lists available at ScienceDirect

Automation in

j ourna l homepage: www.e lsject plans and almost are neglected in traditional planning methods.Consequently, Critical Path Method (CPM) based schedule baselines al-ways experience high uncertainty in execution and require continuous

the interrelationships and dynamics among thesveloping a CPM-based network, it is assumed thators such as weather, overtime, schedule pressother aspect of the problem is related to the changing behavior of theproject system over time (dynamics), e.g., impact of weather conditionon schedule and productivity, and consequence of overtime policy onproductivity. Those dynamics signicantly impact the execution of pro-

coupling with other techniques. The limitations associated with CPMhave been addressed by many researchers [5,6]. The strength of tradi-tional methods lies in their ability to model details at the activity levelor entity (e.g., duration, resources, and cost); however, they neglectrevisions and enhancements to capture the dy

Corresponding author at: Department of BuildinEngineering, Concordia University, Montreal, Quebec H848 2424x7091.

E-mail address: [email protected] (H. Alzraiee)

http://dx.doi.org/10.1016/j.autcon.2014.08.0110926-5805/ 2014 Elsevier B.V. All rights reserved.

Please cite this article as: H. Alzraiee, et al.,(2014), http://dx.doi.org/10.1016/j.autcon.2ts themselves or externalrce of challenges that can

support although without high precision to decision makers. The short-coming of traditional planning tools as discussed previously, can bedue to the surrounding environment, are a sourelationship among those activities [1]the tool for achieving a complex planof the project. However, on many occafail to provide a concise depiction of thehavior. Such failure is attributed to thtainty and the heterogeneous nature o[24]. The interrelationships among theing factors are in reality complex causalear as traditional methods suggest [hen establishing a logicalmodel is expected to beecuting and controllingproject schedule modelst structure and its real be-lexity, dynamics, uncer-nstruction environmentt variables and surround-relationships and not lin-ractions among project

planning from a static and fragmented perspective. The integrity of aproject's strategic and operational variables and their interconnectivityhas never been addressed by traditional methods. In contrast, tradition-almethods break the project system structure into subsystems and dealwith one aspect only (usually operational). Eventually, project plans(e.g., schedules) developed using traditional methods result in modelsthat are discrete in nature and not representative of the system.

Since introducing CPM in themid-50's, and the later evolution of thePrecedence Diagramming Method (PDM) and Program Evaluation andReview Technique (PERT), traditional scheduling tools provide a usefulA project schedule is developed by disintegrating the Work Break- other element(s) to generate behavior. Current planning tools considerDynamic planning of construction activitie

Hani Alzraiee , Tarek Zayed, Osama MoselhiDepartment of Building, Civil, and Environmental Engineering, Concordia University, 1515 Ste.

a b s t r a c ta r t i c l e i n f o

Article history:Received 7 February 2014Received in revised form 24 August 2014Accepted 26 August 2014Available online xxxx

Keywords:Traditional project planning methodSimulationConstructionCPM

Traditional planningmethodecution. However, models detion, cost and productivity. Frelationships that exist amonSDmodels to address the opeoutcomes are realistic projecfactors. Two cases from the ction durations were monitorobserved. The newmethod plimitations associated with tnamics generated during

g, Civil, and Environmental3G 1M8, Canada. Tel.: +1 514

.

Dynamic planning of constru014.08.011using hybrid simulation

erine, W., Montral, Quebec H3G 1M7, Canada

ch as CPM and PERT have been useful tools to manage construction projects' ex-oped using thesemethods often fail to deliver realistic estimates of project dura-re of traditional planning methods is attributed to the uncaptured causaleffecthe project variables. This paper presents a new method that integrates DES andional and soft/strategic variables on a single computation platform. The expectededule networks and enhanced understanding of the interactions of the project'struction sector are used to test and verify this method. Productivity and comple-with/without the impact of factors, in which a signicant discrepancy has beenided better understanding of the project behavior and contributed to overcometional planning methods.

2014 Elsevier B.V. All rights reserved.

Construction

ev ie r .com/ locate /autconminor effects on the outcomes; in addition, the activities are considereddynamically unrelated. This assumption has been proven amajor pitfallof CPM-based networks, and many projects are experiencing cost andschedule overruns due to the simplied and linear approach of address-ing project activity networks. In reality, the interrelationships amongproject inuential factors are more complex than what have been sug-gested by traditional methods [711].

ction activities using hybrid simulation, Automation in Construction

2 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxIn addition to neglecting a project's dynamics, traditional projectplanningmethods have shortcomings in addressing the uncertainty ob-served in estimating/computing costs and duration. For instance, the es-timation of an activity's duration and cost is performed based on adeterministic approach that eventually results in a deterministic num-ber. However, in reality, durations/costs are uncertain and continuouslychange based on the internal/external interactions of a project's ele-ments. As such, those parameters are better represented using a proba-bility distribution rather than a crisp number. This uncertainty has beenaddressed by the PERT method, where three numbers are used to esti-mate most probable costs or durations. However, PERT tends to jumpto results considering a certain allowance for contingencies, and not ad-dressing the reasons behind the cause of the contingencies. It is widelybelieved that contingency allowances are added to account for uncer-tainty and dynamics effects. To address those concerns, simulationtools mainly hybrid ones are considered a promising platform.Methodssuch as Discrete Event Simulation (DES) and SystemDynamics (SD) canbe utilized to account for uncertainty and dynamics. DES is effective inanalyzing the stochastic nature of parameters at the tactical level. How-ever, DES falls short in modeling a project's holistic level, as well as theinterrelationships (feedback process) among its elements. On the otherhand, SD is a powerful tool in addressing DES pitfalls. SD is an excellenttool inmodeling feedback process in a project, which represents the in-teraction between project elements, as well as at the holistic level.

The objective of this paper is to present a planning and schedulingmethod that addresses the uncertainty of estimates (e.g., costs and du-rations) aswell as the project's dynamics, simultaneously. The proposedmethod utilizes a CPM-based network built in a DES environment andintegratedwith an SDmodel. It quanties themanagement policies, de-cisions, and soft variables surrounding a project, and then considerstheir impacts on a schedule built using a traditional planning method.The paper commences by presenting the background to state-of-the-art in scheduling and simulation, in addition to the strength and weak-ness of each technique through a comparative study. The developed hy-brid simulation method is presented in Section 4, followed byimplementation using two cases from the construction sector to testthe applicability of the proposedmethod. In conclusion, the paper sum-marizes the limitations of the planning method, future suggestion andimprovement.

2. Background

Modeling and simulation are powerful tools in representing asystem's real behavior in the virtual world [12]. A good simulationmodel that is capable of mimicking reality should consider four majorrequirements: 1) type of decision levels; 2) the nature of each variable;3) complexity of system; and 4) the relationships between variables[13]. Decision levels in construction projects are divided into twoparts: a) strategic and b) operational [14]. The strategic denition inthis paper is different from the denition pertaining to organizationalmanagement. Strategic Level relates to achieving the project set of ob-jectives within the project policy framework. This involves adjustmentof certain parameters like costs, resources, and time to meet a priorset of goals [7]. On the other hand, the Operational Level is related thenecessary actions taken to meet the project goals set at the StrategicLevel. The Operational Level focuses on daily operational details re-quired at themicro level of the project. Generally, construction projectsinvolve discrete and continuous variables. Those variables are related incausaleffect relationships. The system behavior is mainly generatedbased on the internal and external interactions of those variables withinthe feedback process that result in a more complex but more represen-tative system.

Several techniques and tools were developed to plan, schedule, andcontrol the execution of construction projects and assistmanagement inmaking informed decisions [15]. The scheduling techniques address the

Operational Level (activity), assuming no effects due to uncertainty,

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011dynamics, and interrelationships. Thus, traditional techniques describethe project as top-to-bottom hierarchy through decomposition of pro-ject elements into the smallest acceptable level, where work packagescould be described easily by activities. Thereafter, costs, durations, andresources are estimated, mainly from experience, as deterministic num-bers. Then the project's job logic is demonstrated as a network of activ-ities interconnected based on work sequence and logic. The apparentpurpose of this process is to develop a prototype that depicts the execu-tion of the project in reality. One of themain concerns in such static andlinear philosophy of modeling a project's plan lies in the ability of therestructured activities to behave based on assumptions considered atthe project's decomposition stage. Furthermore, management in realityis dynamic and responsive to new changes to keep a project on track,rather than adhering to the original plans. Those original plans aretargets, or baselines, of themanagement.When those targets are threat-ened by overrun, then management actions are triggered to streamlinea project toward its stated targets. Hence, traditional planningmethods,in reality, produce static baseline plans, which are implemented in a dy-namic environment. Those dynamics are resultants from the causaleffect feedback loops that characterize any system with changing be-havior over time. Researchers have addressed the limitations associatedwith traditional planning methods, such as quantifying subjective fac-tors to better estimate project completion duration [16], developingproject schedule in a DES environment to generate comprehensive in-formation for project planning [17], combined DES and continuous sim-ulation to account for factors surrounding the project and uncertainty[8], and many other efforts in this eld. In general terms, the researchconducted in this regard is specic to certain limitations and can be cat-egorized as special purpose models, e.g. weather impact, uncertainty induration estimates. In this paper, the authors present a generic methodand platform that is more capable of addressing the project dynamics,subjective factors, and operational level (activity) than traditional plan-ning methods.

The SD method was introduced by Forrester [18] as a method formodeling and analyzing complex system behavior in industrial man-agement. The method has been used solely or coupled with othermethods in different elds of social science where a holistic view andfeedback process are critical in understating the evolution of the systembehavior [1922]. The SDmodel aims to capture the feedback processesresponsible to the system behavior within a predened boundary.When a project management team strives to close the gaps betweenproject actual performance and preset targets, such practice is an appli-cation of one principal of SD in project management and control [23].The feedback process experienced in Case Study No.1 shown in Fig. 1is considered to elaborate on the dynamics process as generated duringexecution.

Productivity of engineers is inuenced by many factors such as highschedule pressure, skill level, overtime, and rework. When project exe-cution starts, the expected output is slow and takes the pattern oframping up in the rst 1/5 of project life. The ramping up continuesuntil normal productivity level is reached. The case study being ana-lyzed had the completion duration underestimated; however, it wasstill required to nish the project on time. Many factors played a roleat this stage. For instance high schedule pressure had caused a needfor increase drawing production beyond normal limits. When actualproductivity of a project falls behind the perceived required productiv-ity, the anticipated completion date becomes invisible. Consequently,management must adopt certain policies to reduce the adverse effectsof productivity loss. These policies can be overtime, hiring newworkers,extending the project completion duration to improve productivity, etc.in order to attempt to nish the project on time. Another aspect that isof concern is that not all work completedmeets quality requirements. Arework cycle during construction phase is inevitable, and initial errorcorrection may cause a secondary errors. The dynamics generated be-tween these factors and other factors extends the project completion

duration and creates reinforcing and balancing loops. For instance, one


project

deadline

estimated

completion time

schedule pressure

remaining time in

schedule

+-

+

+

error generation ++ R+

t ac

uctiv

overtime+

+

effect of morale

days lost from

schedule

productivity loss

+

+

B

+

-

+

time needed+ -

R

pro

3H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxof the loops shown in Fig. 1 depictswhat happenswhen project comple-tion duration is underestimated. As can be seen at the top of the gure,two external factors affect the schedule pressure, estimated comple-tion time and remaining time of work already started. When theschedule pressure rate increases to a level that impacts productivity(e.g. greater than 1.5), different polices can be applied to overcomethis effect. This process can be overcome either be considering workingovertime or hiring new workers. Signicant overtime or hiring newworkers has impact on the quality of work. In both cases, more reworkand hence increasing scope of work to be completed and fatigue levelwill also be at a higher level. This cycle of cause-and-effect will continue

rate perceived

completion rate

workremaining

work witherrors

++

+

work completed

with error

+

-

R

B

projec

prod+

Fig. 1. Feedback processes in ato a point where another feedback loop must be enforced to counterthese adverse effects. For instance, training, incentives, and revisingthe project completion date, could be solution to balance the negativeeffect of schedule pressure on productivity. In the case study being ana-lyzed, no such polices where considered to neutralize the adverse ef-fects of the negative effect loops.

3. Planning methods comparison

In this section, the differences between core characteristics of thetraditional planning methods are presented. The purpose is to providea clear and concise focus on the main differences so that strength ofeach method can be utilized in developing the proposed method. Asstated earlier, planning methods can be classied as methods that ad-dress the project process level, uncertainty, dynamics, and interaction.There aremany studies in literature that compare and contrast planning

Table 1Comparison between planning methods.

Perspective Traditional method CPM-network DES method

Focus Activity OperationLevel of details High details High detailsBehavior Linear StochasticModel type Interrelated but distinct packages Interrelated bData type Quantitative QuantitativeState change Discrete At discrete poComplexity Specic Narrow and fo

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011and simulationmethods [24,25].We summarize these ndings in sevenmain perspectives as shown in Table 1. The CPM-network and DESmethods are appropriate for modeling issues that are operational infocus, reductionism in perspective, quantitative in nature, discrete inchange, and narrow in details. The only difference between the CPM-network and DES is the later addresses uncertainty. On the other side,the SD method is appropriate for problems that are of strategic/contextfocus, holistic in prospective, qualitative in nature, continuous in behav-ior, and broad in details [24]. Themain strength of SDmodelingmethodis its ability to mitigate the limitations associated with CPM-networkand DES [25]. Thus, both SD and DES/CPM can be viewed as a comple-

projectscope

tual

ity

-

+

new

workforce

+

-

fatigue

+

-

productivity noise

-

drawings quality--

-

ject execution cycle (Case 1).mentary to one another and the limitations associated with each canbe overcome when both methods are integrated. From analysis of thestrengths and shortcomings of each method, an integrative platformthat builds on the strengths of each method will inevitably enhancethe practice of project planning and thereafter execution and control.

In contrast to CPM-networks and DES, SD modeling and analysisoffer a different perspective on understanding system behavior. This isbecause an SD model developer rst understands the underlying inu-ences responsible on the outcomes, while a developer of CPM-networkstries to anticipate to the outcomes without considering the underlyinginuences. In CPM, the outcomes are usually computedbased on severalapproximations that eventually neglect an important aspect of theproblem, such as, for example the surrounding environment. The needto have a hybrid-planning platform is immense as results of the increas-ing complexity of construction projects and their planning became evi-dent. Hybrid planning can be achieved in different ways based on the

SD method

Holistic and feedbacksLittle detailsDeterministic

ut distinct packages Continuous owQualitative

ints in time Continuouscus on complexity and details Wider focus, general and abstract system


project management team needs. Rodrigues and Bowers [7] suggestedthree approaches that can be utilized to develop a comprehensiveproject-planning model:

1- A more sophisticated network model including the feedback pro-cesses and detailed mechanisms for modeling activity durationsand costs in order to reect the underlying inuences,

2- A more detailed and phased SD model, and3- Adopting lessons learned from SDmodels in the forms of set of rules

for use in planning and estimation.

The three aforementioned approaches attempt to utilize SD in ad-dressing the two-decision Levels in projects (Operational and Strategic).Attempts have been successful at strategic level modeling; however, atthe Operational Level, using SD has not been proved sound despite itsuse in limited and small applications to model the process level. When-

simulation environment. Therefore, the data ows from the SD modelto theDESmodels through the interface variables. In CaseNo. 2, the pur-pose is to study the inuence of operational level variables on the globalSDmodel. Next step involves identifying the interface variables and theinteractions direction and then variables are described using the math-ematical formalism.

4.3. Formalism of DES and SD models

Simulation modeling is not accomplished by directly writing out adynamic system structure, but indirectly, by using system specicationformalism. System specication formalism is a shorthand for specifyinga system [26]. In this paper, formalism is the method used to describethe simulation modules to the Executer. For the purpose of the integra-tion of DES and SD models, formalism must specify the followingproperties:

1. Model type: whether it is DES or SD.2. Input variable (receiver) and source of the input variable.3. Output variable (sender) and source of the output variable.4. Synchronization function that describes the simulation time man-

agement and the interfacing variables.

The aforementioned four requirements are summarized in Eq. (1).

Hybrid DES&SD model I;O;M; S 1

Where:

I: Set of inputs

Ready to formalize interface variables

End

Fig. 3. Building DES and SD models for a hybrid simulation model.

4 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxever SD model has been used at the Operational Level, the model be-came too complex and involved tedious work. In addition, in caseswhere SD modeling was used at the Operational Level, the modelersbroke down the whole project system into subsystems. Such practiceviolates one of the fundamental principles of SD (holistic view). There-fore, it is more appropriate to use each simulation method in areaswhere it shows strength, consistency, and simplicity; otherwise, the ap-plicability of the method by the end-user becomes cumbersome andinappropriate.

4. Methodology

4.1. Integrating DES and SD

The systematic process of Integrating DES with SD is summarized inFig. 2. The method consists of: (1) developing DES and SD models,(2) formalism to describe the models, (3) synchronizing DES and SDmodels, and (4) execution platform (Executer).

4.2. Developing simulation models and identifying interface variables

Step 1 involves developing simulation models and selecting inter-face variables through which mapping between the variables of bothmodels will take place as shown in Fig. 3. The DES model will addressthe project schedule network at the operational level while the SDmodel will be concerned with quantifying the dynamics generatedfrom the interactions of the signicant factors. The interactions betweenthe two models will occur through the interface variables that receivedata from sender variables and deliver data to receiver variables. Thedata exchange between the simulation models is unilateral. Thismeans the hybrid model structure that consists of DES and SD willgovern this process. For instance, in this paper, the purpose ofmodeling Case No. 1 is to study the impact of the surroundingfactors and strategic variables on a CPM-network developed in a DES

Developing Hybrid Simulation Model

Executer

Formalism of DES and SD

Synchronization of Simulation Clocks

Implementation

InputFig. 2. Hybrid simulation framework.

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011Build Hybrid simulation modelSD/DES

Build new SD/DES model

Define model boundary, function & rolefor DES_SD system

Define inputs/outputs and originof data source

Are Inputs /outputs consistent with modelfunction and boundary

Define the interface variables in SD/DESmodel

Are selected points consistent withmodel objectives

Yes

No

No

Yes

Identify model hybridstructure

Start O: Set of outputs


ulation clock advancement, save all data and perform DES and SDmod-ules interfacing. Otherwise, eliminate saved resources and entity dataand return to re-allocate the next process and its entities, attributesand resources. In the DES model, events having occurrence time lessthan the TB1 nish their processes before the interfacing of variablescan take place, hence their data are not captured in the next earliestscheduled interfacing, but their effects are propagated to the secondevent. Therefore, in order to avoid events that start and nish beforethe end of the TB, it is advised to set the SD TIME STEP less than orequal to the lowest expected event time in the model.

After the interfacing is accomplished, all saved data at the end of TB1(interface timepoint) of active or idle resources, are used by theDES en-gine for the next round of computations that begins by the commence-ment of TB2 and continue in the same sequence explained in TB1. Thetime point between the end of TB1 and start of TB2 is the point wherethe simulation clock resumes the progressing of model simulation.The algorithmcontinues until themodel reaches the initially set simula-

5H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxx M: Set of modules (DES or SD) S: Synchronization process (time and interfacing)

The generic Eq. (1) provides a base for deriving Eq. (1.1) to representeither DES or SDmodel to the simulation Executer. The proposed simu-lation Executer requires three sets and two functions to describe DES orSD modules (m), as shown in Eq. (1.1).

m mt ; vall;min;mou; Tb 1:1

Where:

mt: Module type (SD or DES) vall: Set of all interface variables in the module min: Set of module input variables (receiver), described by Eq. (1.2). mou: Set of module output variables (sender), described by Eq. (1.3). Tb: Time point where the interfacing of variables occur

min m; vi;ms; opms;md =mM; viVM ;msM; opms outportsall;mdvalln o

1:2

Where:

vi: Input variables to modulem ms: The module source from which input variables are imported opms: The output port in ms from which the input variables areimported to m module

md: Describes the variables inm that need to use the input variablesfromms. (md describes the variable input vi input port (ipm) inm, thevariables in m need to use vi and the time point in the simulationclock where the interfacing of variables occur)

M: Describes the simulation model (DES and SD) in the hybrid envi-ronment (hybrid model)

VM: All variables in modelM outportsall: Set of all output ports in m vall: All variables in m

mou m;op; ov;

=mM; op outportm; ov valln o

1:3

Where:

op: Output port in m ov: Output variable given through op

4.3.0.1. Simulation clock synchronization. Timemanagement or synchro-nizationmeans the execution of events that occur in distributed simu-lation in a specied order that ensures repeated executions of asimulation produces exactly the same results under the same inputs[27]. The simulation clock of the DES method advances in a differentfashion than the SD method. In DES, the simulation time is driven bythe occurrence of events and subsequently system states are updated.Events usually occur at unequal time intervals. In SD, the simulationclock advances at equal time intervals and system states are updatedat the end of each time interval.

The proposed synchronization method utilizes the concept of TimeBucket [28,29]. It divides the hybrid simulation time of length L intoequal time intervals called Time Buckets (Tb). At the end of these time in-tervals, interfacing of simulation models takes place. Tb should be smallenough to capture any signicant changes in system state and largeenough to discard unnecessary overhead computations. The rationalebehind using the Time Bucket is that SD updates states at equal andpre-known time intervals while DES updates states at the occurrenceof events. Those events generally occur at unequal time intervals; there-

fore, it is easy to trace states of the simulation model at stipulated time

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011points, rather than at the occurrence of events. Tb is set to be equal to theSD model STEP TIME as shown in Eq. (2) and Fig. 4.

Tb hybrid model SD model STEP TIME 2

At the start of the simulation time length L, the proposed hybrid sim-ulation method initializes the simulation clocks of DES and SD engines,as well as, the variables data. Now, DES_SD code is positioned to ad-vance the simulation clock to Tb1, where,

Tb Tb1 Tb2 :Tbn 3

At the end of Tb1, the interface of the variables and their respectivedata exchange occurs between DES and SD as shown in Fig. 4. Thedata ow direction between the two models is based on the structureof the hybrid model through the interface variables. Upon completionof this process, the hybrid DES_SD advances the simulation clock toTb2 and again at the end of the Tb2 interfacing of variables occur. Theevents of the synchronization process that take place between Tb1&Tb2 are illustrated in the algorithm shown in Fig. 5 [30]. The developedalgorithmworks as follows. Initially, for the DES engine to start advanc-ing the simulation time, a condition such as, the required resources andentities should be available at the start of TB1. Now the simulation is inposition to start advancing at the beginning of TB1, if entities seize therequired resources, then all data of active resources and entities in thesimulation model are read and saved, otherwise, idle resources dataare read and saved. If the process involving the active resources hasnot nished processing the entity at the end of TB1, then pause DES sim-

Tb1

Simulation length L

Tb7Tb6Tb5Tb4Tb3Tb2

SD variable state

update behaviourDES_SD

Hybrid Stateafter variables

interfacing

Stateupdate

DES variable stateupdate behaviour

Fig. 4. Synchronization of DES and SD models using time bucket.tion time of length L and then terminates the simulation run.


saves and data

6 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxStart

Advance simulation clock

Resources and Entities areavailable at start of Tb

Do entities seizeresources

Read and save resources IDRead and save entity IDRead and save queue IDRead and save queue lengthRead and save start processing timeRead and save server timeRead and save served resources time

Activeresources

Is entity processingcompleted before end of Tb?

No

Pauses DES simulation clockadvancement, save all data stated

and perform models interface

Eliminateresourceentities

Yes

No

Yes4.4. Executer

The Executer is the VB code developed to integrate DES and SDmodels through utilizing the components of the developed hybrid sim-ulation method. It focuses on integrating and controlling the hybridsimulation model. The Executer interacts with DES and SD simulationengines to facilitate the integration process based on the developedmathematical formalism, the synchronization protocol, and the discretesimulation clock algorithm. The Executer is responsible for performingthe following tasks:

1. Providing the user interface layer that allows inputs and outputs asrequired by the user.

2. Providing models data importexport management. The selected in-terface variables that are designated to receive or share their valuesare specied in the Executer.

3. Implementing the DES simulation clock-advancing algorithm.

The aforementioned components of the simulation method are uti-lized and integrated to develop the hybrid simulation system shownin Fig. 6. The DES and SD simulation models are developed using DES(Stroboscope) and SD (Vensim) engines respectively, the lower part ofFig. 6. The input information for the hybrid simulation model, such assimulation run length, denition of the interface variables, hybridmodel structure etc. are entered through the interface layer. As soonas the simulation engine advances the simulation time and reachesthe dened time step, synchronization of the interface variables takes

Resume simulation starting from all savedat end of Tb with considering the new v

resulted from the models interfa

Clear all saved data

End

Interface Completed

Fig. 5. Algorithm to control advan

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011Idle resources

Yes

Eliminate savedresources data

Read and save resources IDRead and save resources idle stateRead and save queue length of IdleresourcesRead and save times

Is idle state of resourcechanged before Tb end?

No

Pauses DES simulation clockadvancement, save all data stated and

perform models interface

d

Tb+1place based on the developed algorithm and themessages exchange se-quence. The structure of the hybrid simulation model plays the mainrole in executing and progressing the simulation model computation.For instance, if the SD model is designed to act as a global model, andcertain variables within the SDmodel need to be computed by the oper-ational level model (DES) and exported to the SD model, then the DESengine will compute those variables, dump them into a spreadsheetfrom where the SD model will import and receive those variables atevery time step. Finally, the outputs of the simulationmodel such as pro-ject completion duration, productivity, and cost are exported into PDFformat for end user analysis.

5. Implementation

The proposed hybrid simulation method is implemented in twoways, using two different case studies.

1- In the rst case study, the purpose is to investigate the impact of thesurrounding factors on a CPM-network. This means, using the SDmodel to quantify certain factors, then impact the CPM-networkwith those factors to monitor the changes on duration and produc-tivity, and,

2- In the second case study, the logic is inversed by using another casestudy (earthmovingoperations). Thewhole project ismodeled usingthe SD model except at the operational level where DES modeling isused to quantify the operational variables. The productivity of each

valuesalues

ce

cement of simulation clock.


Model

7H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxSimulation dataInterface variableSimulation lengthHybrid structure selection

Interface Layer

DES_SD Executer

Executer DES SD Model

Tb1

(1)

(10)

(8)

(9)

(5)

(4)

(7)

(3)

(2)

(6)

Hybrid DES_SD = (I, O, M, S)

Where:I: Set of inputsO: Set of outputsM: Set of modules (DES or SD)S: Synchronization process

(time and interfacing)m = (mt, vall, min, mou, Tb)

Excel

Applicationoperation (excavation, hauling, and dumping) is inputted throughthe interface variables into the SDmodel to get impacted by the pro-ject dynamics and subjective factors.

5.1. Case Study1: engineering drawings production by a design rm

The proposed planning method is implemented as described in themethodology section. An SD model is developed to capture the effectof the surrounding factors and dynamics such as limitation of skilledlabor, rework cycle, and schedule pressure (the paper focuses on theidea itself rather than explaining the development of the SD modelstructure). The SD model creates a dynamic framework that exhibitsthe classic characteristics of a project's dynamics. Within this frame-work, a CPM-based network is developed in a DES environment to de-scribe the job logic and compute the project's completion durationconsidering uncertainty. The implementation of the case study in thesimulation environment is demonstrated in Fig. 7. The project scope isdecomposed into smaller units to develop the work breakdown struc-ture (WBS), fromwhich activities are identied. Each activity's durationand cost are inputted as probability distributions. ProbSched is utilizedto develop the CPM network [31], as shown in Fig. 7. The ProSched isa probabilistic scheduling package that uses Stroboscope as its engine

Operational Layer Stra

Rocks to Move

Truck Wt.

Loader Idel

Load

Uniform [2,2.2]

HaulTriangular [4.9,5.6,5.7]

Return

Uniform [3.4,3.6]

>=38 , 38

1

>0 , 1

>0 , 1

1

Dumped Rocks

38

Dump

Uniform [1.4,1.6]

SynchronizationFormalization

Specify Hybrid Structure and Interfac

DESOperationallevel

SDStrateand colevel

DES Engine

Fig. 6. Architecture of the hy

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011ProductivityProject CostSchedule

Project performance

Start

Start advance simulation clock

Resources and Entities are

available at start of Tb

Do entities seize

resources

Read and save resources ID

Read and save entity ID

Read and save queue ID

Read and save queue length

Read and save start processing

time

Read and save server time

Read and save served resources

time

Active

resources

Idle resources

Is entity processing

completed before end of Tb ?

Yes

No

Eliminate saved

resources data

Pauses DES simulation clock

advancement , save all data stated

and perform modules interface

Read and save resources ID

Read and save resources idle

state

Read and save queue length of

Idle resources

Read and save times

Is ideal state of resource

changed before Tb end ?

No

Keep saved

resources data

Eliminate saved

resources and

entities data

Resume simulation starting from all

saved values at end of Tb

Clear all saved data

End

Tb+1

Yes

No

Yes

Actual releassed production

600

450

300

150

Total cost

Time (Hour)

20 M

15 M

10 M

5 M

0

$

0 25 50 75 100 125 150 175 200 225 250

Total cost : 1

m3/H

ourand Microsoft Visio as its Graphical User Interface. ProbSched allowsthe denition of CPM networks where the cost and duration of each ac-tivity can be dened probabilistically. ProbSched produces graphicaloutput to indicate the criticality of each activity and statistics of theearly and late times and oats of each activity and the project. The sec-ond component of the implementation involves developing SD modelresponsible on modeling the subjective factors. The SD model is devel-oped using Vensim Software Package from Ventana Systems, Inc [32].The Gantt chart shown in the gure is a sample output of the hybridmodel and represents the schedule network of activities after impactby the SD model.

5.1.1. Data of Case Study1A design rmwas hired by a client to produce a variety of engineer-

ing drawings for expansion of Oil Sands existing facilities. The engineer-ing drawings were: 1) civil (Task1); 2) mechanical and structural(Task2); 3) electrical (Task3), and 4) piping (Task4). The designs wereprepared by different engineering departments within in the samerm. A resources loaded schedule that depicts the logical sequenceamong the four activities was developed using the traditional methodsand provided to the client. The main purpose of the schedule was to es-timate the completion durations of the project, and track the progress of

tegic/Context layer

Sim clock Adv. Algorithm

e Variables

gicntext

0

Time (Hour)

SD Engine

0 25 50 75 100 125 150 175 200 225 250

Actual releassed production : 1

brid simulation system.


CPM or PDM

Gantt Chart of Simulated CPMnetwork after Impacted by SDmodelDiscrete Simulation Engine (CPM)

8 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxActivities

Durationscompleted drawings. Based on the CPM schedule, the project was ex-pected to be completed in 70 weeks; however, the actual project com-pletion duration exceeded this initial estimate by 40% (28 weeks).Therefore, this was a base for a good case study to investigate, model,and analyze. The data collections also involved understanding the cir-cumstance surrounding the projects, such as maximum available man-power, overtimes, management policies, and schedule pressure levelas this helps in understanding the dynamics of the project system. Therm used units system to quantify the efforts needed to nish a singledrawing (e.g., a drawing requires 2000 units of work to complete).The productivity of individuals is measured by the number of drawingscompleted and checked per week. The data of the four tasks is shown inTable 2. Each Task duration was estimated using three points: optimis-tic, most likely, and pessimistic. The maximum available skilled work-force dedicated to this project was 160. It was recorded by the projectmanager that maximum production per drawing is reached when 20%of the drawing is complete and declined in the last 20%. This

Activities Information

Costs Resource

Fig. 7. Layout of the propo

Table 2Case study data.

Task name Task scope in units Triangular probability dist.of task duration in weeks

Task1(T1) 40,000 20, 20.4, 20.9Task2(T2) 50,000 20, 20.2,20.6Task3(T3) 18,000 10, 11.7, 12Task4(T4) 100,000 39.9, 40.5, 40.8

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011Scope Task is

Scope Task is Done C

Scope Task Start Flag C

Percentage of

Compacted Soil

Scope Start

Spread Soil

Rework

Error Rate Rework rate

Avg, QP

Duration Quality Check

Process Rate

Quality Process

RatePerceived

Quality Rate

Rework Process

Rate Duration

Rework process

rate

Perceived

Rework RateError Generation

Rate

Total Rework

Project Policies andinformationphenomenon is well known in construction work execution and isdepicted in Fig. 8.

5.1.2. Models development, result and discussionThe developed hybrid model consists of a CPM-based network

(Fig. 9) and SD model. The purpose of the CPM network is to capturethe operational level parameters such as durations and the logical se-quence of the activities, while the SD model will capture the dynamicsgenerated from themodel's variables due to internal and external inter-actions. The CPM network is compiled in DES engine (Stroboscope)using a ProbSced add-on application. The activity start time is similarto the activity start date in the conventional schedule network;

pread

Active C

Soil toCompact

Soil Compactedand ready forQuality Check

Compaction

Rate

Max Compaction Rate

Total SoilCompacted and

Ready for QualityCheck

Percentage ofCompaction

Final Work

CompletedProductivity Rate

Total Project Work of Soil,Hauled, Dumped, Spread, and

Compacted

Effect of SchedulePressure onProductivity

Lookup

effect of schedulepressure onproductivity

SD Simulation Engine (SD Model)

sed planning system.

20% 80%

1

Per

form

ance

Fac

tor

Percentage of drawingCompletion

Fig. 8. Planned work prole.


Gross productivity task normal productivity task effect of morale on productivity task effect offatigue on productivity task effect of schedule pressure onproductivity task Units : dwg=person=week 5

Undiscovered Rework task gross completion rate task

Task 1

Task 4

Task 3Task 2

9H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxhowever, since the activities are bonded to the simulation time length,we will refer to the start date as the start time as shown in column(1) in Table 3. The durations for every activity are inputted into theCPM-discrete simulation network as a triangular distribution. Themodel ran for 500 cycles and the average duration for each Task werecomputed as shown in column (2) in Table 3. The total completion du-ration as computed by the CPM network in the DES environment was70.9 weeks. It was noticed that the difference between the determinis-tic and stochastic total project duration isminor, and thiswas attributedto the minor differences in the three estimated durations of theactivities.

The next stage was developing an SD model to represent theproject's dynamics based on the causal loops diagram shown in Fig. 1.These loops are rigorously researched and cited in literature by manyresearchers [21,23]. The developed SDmodel is composed of four mod-ules (workow, rework, quality, and labor demand). The workowmodule describes the workow from execution to completion. A re-work cycle module is added to account for work that does not passthe quality standards and needs to be reworked. The scope of reworkis returned to the initial stock for further processing. The schedule pres-sure resulting from low productivity and increasing rework is capturedas well. The strategy of the rm in addressing mounting schedule pres-sure was by hiring likely additional workforce or considering overtimefor the current staff. The SD model structure was developed in such away to capture the effects of schedule pressure, fatigue, overtime,and rework cycle on quality of work and project completion duration.All of these interactions are captured within the causaleffect loopsthat depicts the dynamics specied in the SD model framework

Simulation Time (weeks)20 40 60 80

Fig. 9. Gantt chart of the tasks before simulation.(Fig. 1). Samples of the equations used are shown in the equations num-bered 4 to 9.

Work Completed Correctly task gross completion rate task work quality task ;0

Units : dwg4

Table 3Simulation data inputs for the CPMDES schedule.

Task Name Task start timeweeks(1)

DES Simulated averageduration in weeks (2)

Complin Wee

Task1 0 20.5 20.5Task2 20 20.35 40.35Task3 40 11.4 51.4Task4 30 40.90 70.90

70.90

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011 1work quality task rework discovery task ;0: 6

Rework discovery task MAX0;MINUndiscovered Rework task =TIME STEP; UndiscoveredRework task =rework discovery time task SUMdownstream reworkdisc downstream!; task Un3its : dwg=week 7

Work remaining task MAX0;TASK DEFINITION task reported work complete task Units : dwg 8

Reported work complete task Work Completed Correctly task Undiscovered Rework task Units : dwg 9

The SDmodel was simulated, and a sample of the results is shown inFig. 10. The project completion duration estimated by the discrete sim-ulation model had been extended from 70.9 weeks to 92 weeks,Fig. 10a. This represents an additional of 32% to the project plannedcompletion duration. In addition, it can be observed in the gure thatthe start of Task 4 was delayed from the 30th week to the 48th week.This delay is attributed to the interesting pattern observed in Fig. 10cd during this period. In the gure, the quality standard of the executeddrawings was degraded in the period from the 25th week to the 48thweek. The poor quality evident is mainly due to the delays of startingTask 4, which demanded more resources than was planned. Conse-quently, limited resources and signicant delay increased the schedulepressure; therefore, a higher productivity rate was required from theoverstretched resources. Increasing the productivity rate beyond the al-lowable limit caused some drawings to be nished not per acceptedquality standards. Therefore, it can be observed in Fig. 10d that thenum-ber of the drawings that need to be reworked has increased to themax-imum level between the period of the 30th week and the 45th week.

etion milestonesks (3)

Predecessor requiredto start (4)

Productivity of engineerdrawing/week (5)

20T1 25T1,T2 15T1,T2 25ction activities using hybrid simulation, Automation in Construction

Run1

task is active[task]

TASK1

TASK2

TASK3

60,000

45,000

30,000

15,000

04 4 4 4 4 4 4

44

44

4

3 3 3 3 3 33 3 3 3 3 3

2 2 2

2

2

2 2 2 2 2 2 2 2

11

1 1 1 1 1 1 1 1 1 1 1

Cum

Cum

Cum

Cum

4

3

2

1

Units

Un

Units

10 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxa) Gantt chart of completed Tasks

0 25 50 75 100

Time (Week)

TASK4

1

0.9

0.8

0.7

0.6

4 44 4

4

4

4 4 4 4

4

43 3

3

3 3 3

3 3 3 3 3 3

2 2 22

2

2 2 2 2 2 2 211

1

1 1 1 1 1 1 1 1 1UnitsThis added extra scope of rework increased the initial scope of work;consequently, increasing the schedule pressure, overtime demand,and fatigue level. The project's execution exhausted 50% of its planneddurationwhile the actual productivity was not as perceived at the plan-ning stage. These dynamics triggered the causaleffect loops that con-trol the workforce, thus demanding additional workforce. However, atthis stage, themaximumavailable resources had been attained. The pos-itive inuence of this loop was frozen, which caused other loops of nega-tive inuence to occur, such as the schedule pressure loop to build up.Higher schedule pressure, constrained by maximum resources triggeredthe need for overtime as a policy to increase productivity, Fig. 10.e. Over-time is associated with labor fatigue (physically and mentally), and con-sequently, this increased the aws in the drawing production (Fig. 10d).The accumulated impacts of those factors extended the project duration

c) Quality of executed drawings

e) Productivity per Task

0 10 20 30 40 50 60 70 80 90 100

Time (Week)

3

46,000

4,500

3,000

1,500

04 4 4 4 4 4 4

4 4 4 44

43 3 3 3 33

33 3 3 3 3 32 2 2

2

2

22 2 2 2 2 2 2 2

11 1

1 1 1 1 1 1 1 1 1 1 10 10 20 30 40 50 60 70 80 90 100

Time (Week)

10

UnitsUnits

Fig. 10. Sample of the simu

Table 4Scope of work.

Soil type & layers Stage 1 m3 Stage 2 m3 Stage 3 m3 Loose

Rock 192,700 3,209,400 1,602,900 1.66Granular 14,500 286,500 139,000 1.72Moraine 29,200 555,900 269,900 1.66Total 236,400 4,051,800 2,011,800 1.6

6.3 million m3

Excavation (river Bed)

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011b) Cumulative of completed work units per Task

0 10 20 30 40 50 60 70 80 90 100

Time (Week)

ulative Of Completed Work Units Per Task[TASK1] : Run1 dwg1 1 1 1

ulative Of Completed Work Units Per Task[TASK2] : Run1 dwg2 2 2 2

ulative Of Completed Work Units Per Task[TASK3] : Run1 dwg3 3 3

ulative Of Completed Work Units Per Task[TASK4] : Run1 dwg4 4 4

,000

,000

,000

,000

0 44 4 4

43

2

2

211

itsto 92 week (32% higher than planned). The nal accumulation of the ex-ecuted work is shown in Fig. 10f, which shows an s-curve behavior.

Based on these outcomes, the causaleffect loops of the modelshould be reviewed and revised polices should be considered to addressthe negative results observed in the outcomes. A remedy to the negativeimpacts of certain factors in the model can be attained by tracing theproblematic loops and minimizing their effects as well as maximizingthe impact of the favorable loops.

5.2. Case Study2: earthmoving operations

5.2.1. Case study descriptionIn the second case study, the earthmoving operations involved in a

dam construction were modeled and simulated using the proposed

d) Undiscovered rework

f) Accumulated completed units

4 4 4 4 4 43 3 3 3 3 3 3 3 3 3 32 2 2 2 2 2 2 2 21 1 1 1 1 1 1 1 1 10 10 20 30 40 50 60 70 80 90 100

Time (Week)

2 2 2 2

3 3 3 3

4 4 4 4

0,000

75,000

50,000

25,000

01

11

11

1

1

11 1

1 11 1

11

1

0 10 20 30 40 50 60 70 80 90 100

Time (Week)

lation model outputs.

density (t/m3) Bank density (t/m3) Load factor % Total of soil m3

2.73 80 5,005,0001.93 90 440,0002.02 100 855,0002.4 100 6,300,000

1,038,000


causaleffect structure whose values are independent from the states

or variables that inuence the stock level or accumulation. Decoupling

Table 5Fleet conguration and characteristics.

HauledMaterial

HaulerModel

LoaderModel

Hauled Soils(ton)

Load activityTime Dist. (m)

Haul activityTime Dist. (m)

Dump activityTime Dist. (m)

Return activity TimeDistribution (m)

Rock 777D 992G 81.67 (3.94, 4.15, 4.57) (4.3, 4.53, 4.98) (1.9, 2.2) (3.17, 3.34, 3.67)Moraine 773D 990 SII 45.82 (3.01,3.2, 3.32) (19.47, 20.5, 22.55) (1.6, 1.9) (16.71, 17.59, 19.35)Granular 769 C 988F 34.36 (2.3, 2.42, 2.5) (30.6, 32.34, 35.57) (1.3, 1.5) (25.85, 26.51, 29.16)River Bed Soil 777D 375L 51.41 (4.26, 4.48, 4.93) (5.32, 5.6, 6.16) (1.6, 1.9) (2.86, 3.01, 3.31)

11H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxdynamic planning [33]. The scope of the work had two main parts.The rst was excavating and preparing the riverbed for the dam'sfoundation, while the second was backlling three types of soils toserve as the main dam structure in restraining the water. The scope ofwork of excavation from riverbed was to remove, haul, and dump1.038 million m3 of soil. Thereafter, backlling and compacting6.3 million m3 of three soil types: 1) compacted moraine (clay) 2);granular (sand and gravel); and 3) rock, as shown in Table 4. The backlloperations involved processes such as loading, hauling, dumping,spreading, and compacting of the soil.

5.2.2. Fleet conguration and duration of operationsThe eet conguration used to execute the project scope is shown in

Table 5. Information related to equipment was obtained from themanufacturing specication manual of Caterpillar [34]. The eet ofequipment included three types of haulers (777D, 773D and 769C)served by three types of loaders (992G, 990SII, and 988F), respectively.The haulers' travel times under loaded and unloaded conditions corre-sponding to certain speeds were calculated by using manufacturer'scharts (RimpullSpeedGradeability and Brake Performance Charts),total resistances, and road segment lengths. Duration times needed byloaders to load a specic truck were calculated using loader specica-tion charts and tables.

5.2.3. Elements of simulation modelAs shown in Table 6, the case study is composed of elements related

to operational level and elements related to strategic level. The projectbehavior mainly resulted from those elements and their interactions.At the operational level, the excavation operation involved excavating,loading, hauling, and dumping processeswhile the soil backlling oper-ations involved loading, hauling, dumping, spreading, and compactingprocesses. From a policy and strategic perspective, perceived productiv-ity, weather, overtime, cut depth, road condition and others were criti-cal elements. The elements, whenmodeled and simulated, are expectedto generate the real behavior of the operations in the virtual world. Theclassication into operational/strategic and selection of simulationmethod is performed based on the criteria presented in the backgroundsection.

5.2.4. Developing DES modelsStroboscope [35] is a simulation language used to develop general-

purpose DES models. Processes such as excavating, loading, hauling,dumping, spreading, and compacting are modeled using DES. Ten DESsimulation models were developed for excavation and backlling oper-

ations. The excavation DES model computes the Max Excavation Rate

Table 6Summary of the simulation model elements.

Operations Operational level

Excavation Excavation, Loading, Hauling, d

Rock11,1 Granular12, Moraine13, Rock21, Granular22,Moraine23, Rock31, Granular32, Moraine33

Loading, Hauling, Dumping, ReCompacting

Simulation Method DES & traditional method

1 Rock 11 means, rst layer & rst stage (refer to Table 4).

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011the rate (ow) from the system, stock becomes the source of disequilib-rium in system dynamics [36].

The SD model is composed of four modules: (1) workow module;(2) schedule pressure module; (3) rainfall and road condition module;and (4) cost module. Each module is responsible for modeling the be-havior of the element that it represents. For instance, the workowmodule is responsible for modeling the interactions of the variablesof other variables in the system. Variables categorized as excluded var-iables are cautiously not included in the structure of causaleffect feed-backs. Themodel's boundary of the developed SDmodel is summarizedin Fig. 11.

5.2.5.2. Stocks and ows diagram.Dynamic behavior in SD is raised due tothe principle of stock or level. As the name implies, stock represents avariable state resulting from decisions. Stock is an accumulation charac-terizing the system state, and it generates information upon which de-cisions and actions are based and accumulated. Stock changes onlythrough ows, and creates delays in themodel by accumulating the dif-ference between inow to and outow from the stock. Stock: 1) has amemory; 2) changes the time shape of ow; 3) decouples ow; and4) creates delays. Finally, stock is modeled by themathematical integra-tion of the sum of the ows coming in to the stock and the owsdispatched from the stock. On the other hand, ow represents actionsand theMax Dumping Rate while the backlling DES models computethe Max Dumping Rate, Max Spreading Rate, and Max compaction Rate.Those ve variables are used as input in the SD global model, and laterin the synchronization implementation which will be called interfacevariables. The outcomes of the ten discrete simulation results areshown in Table 7.

5.2.5. Developing the SD model

5.2.5.1. Model boundary. An essential step in developing the SDmodel isdening the model's boundary. This boundary involves selecting thevariables that generate the behavior of interest as specied by themodel's purpose. Variables in the model are classied as endogenous,exogenous, and excluded. Endogenous variables are the main concernof all model variables. They are variables in a causaleffect structurewhose values are determined by the states of other variables in the sys-tem. Exogenous variables are from outside of the model, and unex-plained by the model's feedback structure. They are involved in athat describe the job-logic sequence at execution stage.

Strategic/context level

umping, and Return Cut depth, schedule pressure, road condition, operator skill,soil type, job efciency, weather condition, and overtime.

turn, Spreading, and Schedule pressure, road condition, overtime, rework cycle,soil type, operator skill, weather condition, and overtime.SD


Table 7DES model outputs.

Process Excavation Rock 11 Granular 12 Moraine 13 Rock 21 Granular 22 Moraine 23 Rock 31 Granular 32 Moraine 33

Scope of work (m3) 1,038,000 192,700 14,500 29,200 3,209,400 286,500 555,900 1,602,900 139,000 269,900

Haulers 7 8 10 8 8 10 8 8 10 10

Loaders 2 2 1 1 2 1 1 2 1 1

Bulldozers 2 3 1 1 3 1 1 3 1 1

Compactors 0 3 1 1 3 1 1 3 1 1

Dump(m3/h)

Max 1421.33*/1367.39** 1462.43 216.87 347.88 1462.90 221.28 349.82 1466.84 223.70 424.90Min 1222.65*/1200.56** 1323.11 157.30 260.10 1323.08 158.10 262.01 1319.26 156.76 337.55Average 1320*/1284.52** 1393.21 187.21 304.55 1393 190 306.29 1393.08 190 381.49St. Deviation 34.40*/28.27** 35.20 15.37 22.19 35.36 16.88 22.06 37.25 17.88 22.13

Spread(m3/h)

Max 1462.56 216.94 343.95 1456.75 221.65 349.05 1462.94 221.81 434.98Min 1323.05 157.18 264.07 1329.15 158.26 262.18 1323.15 158.47 327Average 1393.21 187.43 304.55 1393.21 190.32 306.29 1393.08 190.23 381.49St. Deviation 35.16 15.275 20.32 32.16 16.67 22.06 35.25 16.88 27.13

Compact(m3/h)

Max 1464.98 218.26 341.79 1458.77 221.66 351.71 1466.72 221.83 430Min 1321.41 155.15 266.14 1327.09 158.06 260.42 1319.79 158.11 331.00Average 1393.21 187.43 304.55 1393.21 190.68 306.29 1393.08 190 381.49St. Deviation 36.17 16.25 19.32 33.67 16.63 23.06 37.25 16.88 25.13Duration (hour) 808.33 138.44 77 96.79 2303.70 1054.72 1814.44 1151.85 730.42 707.63

Total backll duration = 8504 h. By considering 50% overlapping, duration = 4620 h. * Excavation rate of riverbed, ** Dumping rate of Excavation of riverbed.

12 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxThe workow module is composed of ve structures: 1) loadingdumping; 2) spreading; 3) compaction; 4) rework; and 5) released pro-ductivity as shown in Fig. 12. Those structures describe the realwork ex-ecution sequence. The SDmodel is initialized at Soil to Haul stock. Theinitial value of this stock is the total scope of work (6.3 million m3),modeled mathematically, using the subscript control feature in Vensimto distinguish the different soil types. Then the scope is processed at theDumping Rate ow that represents the impacted eet dumping pro-ductivity. The MaxDumping Rate variable shown in Fig. 12 is the inter-face variable that receives and sends values from DES to SD. Theexported DES variable (sender) represents the ideal rate. This variableis impacted by the causaleffects loops in the SD model to deliver nearactual rates of production. The treated rate ends up in Net DumpingRate (receiver variable). After processed by Dumping Rate, the scopeis accumulated in Soil Dumped stock. Now, soil is ready for the nextstage, which is the spreading and thereafter compaction. The same pro-cedures are followed for Net Spreading Rate and Net Compaction Rate

variables. The quality of compacted soil must be checked based on the

Exclud

Endoge

ExogenSafety

Environmental

Impcat

Cash flow constraints

Project

deadline

scope

Error rate W

i

Overtime

impact

Actual productivity

Project progress

Overtime

Weather

Schedule pressure

Quality

Forecasted

Learning

effect

Fig. 11. SD mode

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011design standards before the nal release. Thus, the compacted soil isstocked at Soil Compacted and Ready for Quality Check stock. The soilthat passes the compaction test is processed through the ProductivityRate ow, and the faulty compacted soil is passed to the Reworkstock for further re-work to assure the required quality. The summationof the ow's Productivity Rate and Rework Rate represents the actualreleased work productivity. The Gantt chart of workow execution isshown in Fig. 13. As observed in the chart, operations are scheduledwith 50% overlapping between scopes. The excavation scope isnot shown in this chart because excavation operation involved onlyexcavation and haulingdumping while the backlling involvedhaulingdumping, spreading, compaction, and quality check.Thus, the structure of the SD for excavation scope should be differentfrom the SD model of the backlling scope.

5.2.6. Identifying the interface variablesThe interface variables are the interface points between the DES andSD simulation models. Those variables are responsible to ensure that

ed

nous

ous

Secondary

error

Impact of

scope change

eather

mpact

Information

flow

Soil type

Equipment

maintenance

Operator skill

production

Project progress

Road condition

Grade of cut

Depth Cut

Workforce

Workforce shortage

l boundary.


hybrid structure functions based on the selected datamapping betweenthe models. Referring to the SD model,Max Dumping Rate,Max Spread-ing Rate, and Max Compaction Rate shown in the red triangular shapes(Fig. 13) are the interface variables. Those variables are the interface

points that act to receive DES input from sender variables and deliverthem to receiver variables in the SD model.

6. Results and analysis

Due to the harsh cold weather at the dam construction location, theproject execution was planned in three phases. Each phase starts onApril and ends by November. In the project-planning phase, several sce-narios can be considered to execute the project. The simulation toolsplay an essential role in this context to show the discrepancy betweenthe different scenarios and assist themanager inmaking informed deci-sions. To test the developed hybrid simulation model, three scenarioswere considered as shown in Table 8. Each scenario was inputted tothemodel and simulated for 6200 h. The initial normal project durationwas calculated by adding up the durations of the nine scopes computedby DES models resulting in a total of 8504 h. In the planning stage, anddue to structural stability of the backll layers, it was considered thatevery two successive activitieswere overlapping by 50% (the successive

0 1550 3100 4650 6200

Time (hr)

Rock11Granular12Moraine13



Fig. 12. Gantt chart of soil backll scope.

Compaction_DES Model Input ZoneLHD_DES Model Input Zone Spreading_DES Model Input Zone

Scope Task is

Active D

Soil to Haul

Scope Size

Scope Task is Done D

Soil Dumped

Dumping

Rate

Total Soil Dumped

Start Task Flag

percentage of

Dumped Soil

Start Scope Task

Percentage

Dumping

Scope ID

Scope Task is

Active S

Soil toSpread

Scope Task is

Done S

Soil SpreadSpreading

Rate

Max Spreading Rate

Total Soil Spread

Scope Task

Flag S

Percentage of

Spread Soil

Start Scope Task

Percentag Spreading

Scope Task is

Active C

Soil toCompact

Scope Task is Done C

Soil Compactedand ready forQuality Check

Compaction

Rate

Max Compaction Rate

Total SoilCompacted and

Ready for QualityCheck

Scope Task Start Flag C

Percentage of

Compacted Soil

Scope StartPercentage ofCompaction

Dumped Soil

Spread Soil

Final Work

CompletedProductivity Rate

Rework

Error Rate Rework rate

Avg, QP

Duration Quality Check

Process Rate

Quality Process

RatePerceived

Quality Rate

Rework Process

Rate Duration

Rework process

rate

Perceived

Rework RateError Generation

Rate

Total Project Work of Soil,Hauled, Dumped, Spread, and

CompactedAverage time

Effect of SchedulePressure onProductivity

Lookup

effect of schedulepressure onproductivity

Total Scope

Total Rework

Max Dumping Rate

Loading-DumpingOperation

Spreading Operation

Compaction and ReworkCycle

Actual FinalReleased

Productivity

productivity

PerceivedProductivity

Project Deadline

Productivity toComplete

Time Available

Schedule Pressure

Fraction Completed

Project is Done

Project was Done

Restart Fraction

Final ProjectCompletion

Duratione and overtime feedback loops.


activity starts as soon as 50% of the precedent activity is completed).Based on that, the project completion duration was calculated as4620 h (refer to Table 7).

Scenario (1) is concerned with generating a hybrid base case thatdemonstrates the behavior in ideal situations. The planned project com-pletion duration was set to 4620 h without considering inuences ofcontext variables and polices. The stochastic value of variables comput-ed by DES models were input into the SD model through interface var-iable as shown in Fig. 14 and the model was simulated for 6200 h. Thebase hybrid model structure and assumption considered for scenario(1) should results in outputs (project duration and productivity) similarto these computed using DES models since no surrounding factors areconsidered. The hybrid base case (scenario 1) resulted in 4620 h projectcompletion duration, which is similar to the DES models computation.In scenario (2), factors such as weather impact, quality level, schedulepressure, and overtime were fed to the model to monitor their impacton the simulation model's outcome. The factors considered in themodel are explained next.

Weather impact: weather and specically precipitation has greatimpact on earthmoving projects. Rainwater has an adverse effect on

the hauling roads and the capability of the eet to function in an opti-mummanner. The impact of precipitation rate on earthmoving projectshad been investigated thoroughly by El-Rayes &Moselhi [37]. The studypresented a quantication method of days and productivity losses dueto the amount of the rainfall. The outcome of the study has been utilizedin this research to predict days lost due to monthly precipitationamount. The averagemonthly precipitation amount for the site location(Sept Ile, Quebec) of the dam has been acquired from the NationalClimate Data and Information Archive-Canada. The data represents theaverage monthly precipitation amount in millimeter for 29 years(19712002).

Schedule pressure: perceived maximum project productivity pertime unit (management policy factor) was calculated based on dividingthe project overall scope of work by the total planned project comple-tion duration. Accounting for weather impact and other factors has in-creased the actual project duration beyond the planned duration. Theincrease in the project duration is calculated at hourly step-time and isdivided by the remaining time from the original planned project dura-tion. The resulted ratio is called schedule pressure ratio, which affectswork quality and productivity. From schedule pressure, the anticipated

Dumping Rate Compaction Rate

m3

m

Table 8Implementation scenarios.

Scenario 1 (Hybrid base case) Scenario 2 Scenario 3

-Project planned duration = 4620 h-Error in compaction quality = 0%-No weather impact-No schedule pressure impact-No overtime and fatigue impact-No depth of cut impact-No adverse road condition

-Project planned duration = 4620 h.-Error in compaction quality = 5%-Weather impact-Schedule pressure impact-Overtime and fatigue impact-No depth of cut impact-No adverse road condition

-Project planned duration = 6000 h.-Error in compaction quality = 5%.-Weather impact-Schedule pressure impact-Overtime and fatigue impact-No depth of cut impact-No adverse road condition

14 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxx2,000

00 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

Spreading Rate2,000

1,000

m3/h

m3/h

9 operations output (Table 7)00 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

dumping rate[ROCK11] : Run1 1 1 1dumping rate[GRANULAR12] : Run1 2 2 2dumping rate[MORAINE13] : Run1 3 3dumping rate[ROCK21] : Run1 4 4dumping rate[GRANULAR22] : Run1 5 5dumping rate[MORAINE23] : Run1 6 6dumping rate[ROCK31] : Run1 7 7 7dumping rate[GRANULAR32] : Run1 8 8dumping rate[MORAINE33] : Run1 9 9 9

Fig. 14. DES model variabl

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.0112,000

1,000

00 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

Productivity Rate

2,000

1,000

/h

3/h00 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

m3/hr1 1 1 1m3/hr2 2 2m3/hr3 3 3 3m3/hr4 4 4 4m3/hr5 5 5 5m3/hr6 6 6 6m3/hr7 7 7 7m3/hr8 8 8 8m3/hr9 9 9 9

es used by SD model.


a) Schedule Pressure b) Overtime required

d)

0

0.1

0.2

0.3

0.4

0.5

00.5

11.5

22.5

33.5

44.5

5

041

583

012

4516

6020

7524

9029

0533

2037

3541

5045

6549

8053

9558

10

Frac

tion

Time (hr)

Scenario 1 Scenario 2 Scenario 3

0

0.5

1

1.5

0

5

10

15

20

25

034

569

010

3513

8017

2520

7024

1527

6031

0534

5037

9541

4044

8548

3051

7555

2058

65

Frac

tions

Time (hr)

Scenario 2 Scenario 1 Scenario 3

1

1

0.

0.

ress

15H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxovertime needed to nish the project is also computed on an hourlybasis. Overtime also affects productivity and its impact was quantiedbased on a reference model from Hanna et al. [38]. The impact of theschedule pressure on the three scenarios is shown in Fig. 15. The sched-ule pressure for scenario (1) as shown in Fig. 15.a is zero, while for sce-nario (2) the schedule pressure increases after 1900 h. In scenario (3),

c) Impact of Fatigue on Productivity

0.9

0.8

0 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

2

3

Fig. 15. Schedule pwhere project planned duration was relaxed from 4620 h to 6000 h,the schedule pressure was maximum at fraction of 0.4. Correspondingto the increase or decrease in schedule pressure, the demand on over-time was increased or was decreased respectively, Fig. 15.b. The sched-ule pressure and overtime have adverse effects on productivity and thiscan be noticed in Fig. 15.c&d.

Depth of the cut: one of the important factors that affect the excava-tor productivity is depth of the cut of the excavated soil. The equipmentoperator faces difculty in lling the bucket of excavator in one passwhen the depth of the cut increases beyond certain depths [39]. Asthe work progresses and the depth of the cut increases, the excavatorproductivity will be affected.

Road surface condition: rutted and soft roads that have higherrolling resistance may affect the hauling duration, and consequently

Actual Final Released Productivity

2,000

1,500

1,000

500

0

0 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

Actual Final Released Productivity : Scenario 1 m3/hr



1

23

Fig. 16. Productivity comparison

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011affect the eet productivity [38]. The scenario that represents the ad-verse site and access road conditions considers good road conditionsfor the rst 16 h of the work execution, then the road surface gets dete-riorated slowly until scheduled maintenance at start time 80 h.

Based on the above discussion, scenario (2) has resulted in a projectduration of 6020h,which represents a 30.30% increase from the original

Impact of Schedule Pressure on Productivity

9

80 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

2

ure and overtime.planned (4620 h). The productivity of the eet has dropped from an av-erage of 1400 m3/h to an average of 1000 m3/h (28.5%). In scenario(3), the planned project duration has been set to be 6000 h, and themodel computed the project duration as 5865 h. Scenario (3) hasbeen subject to the same conditions as in scenario (2) except that theproject duration was relaxed, thus decreasing the adverse impacts ofthe schedule pressure and the overtime on productivity and quality(Fig. 15.c&d). As a result, scenario (3) nished the project earlier thanin scenario (2). Fig. 16 summarizes the differences between the threescenarios in terms of near actual productivity and accumulated workperformed. These three scenarios demonstrate importance of projectdynamics and their impacts on outcomes.

Follow-up reviews and an investigation of real project data con-rmed the obtained results. The model successfully predicted what

Total Project Work of Soil, Hauled, Dumped, Spread, and Compacted

8 M

6 M

4 M

2 M

00 620 1240 1860 2480 3100 3720 4340 4960 5580 6200

Time (hr)

"Total Project Work of Soil, Hauled, Dumped, Spread, and Compacted" : Scenario 1m3



13 2

between the three scenarios.


1998, pp. 12871294.[32] Ventana Systems Inc, Vensim, Massachusetts, Harvard, 2013.

16 H. Alzraiee et al. / Automation in Construction xxx (2014) xxxxxxreally happened in execution. The earthmoving operations of the damconstruction were completed in three years (3 years x7.5 months X30 days x10-hourwork). This makes the total actual project completionduration approximately 6300 h. The hybrid simulationmodel predictedthe total project duration as 6020 h. The difference between the two g-ures was 4.44%. Therefore, it can be said that the hybrid simulationmodel predicted the project completion duration and productivitywith high accuracy.

7. Conclusion

Traditional planning methods are based on deconstructing the pro-ject into individual activities, and then establishing the job logicamong those activities. This results in addressing the construction prob-lem in fragmented and linear fashion. The purpose of any projectmodel,whether it is an SDmodel or a CPMnetwork, is to strive to deliver an un-biased model that captures the likely behavior of a project during theexecution phase, and produces realistic estimates of productivity andproject completion duration. Generally, construction planers addressuctuating productivity by using contingency allowances to estimatethe most likely project completion duration. However, contingency es-timates are subjective, and mainly rely on personal judgment. The pre-vious research indicated that SD modeling is well suited to address thedynamic nature of the interrelated project parameters at the strategiclevel, while traditionalmethods arewell suited formodeling the tacticalaspects of these parameters. Therefore, this paper has addressed thoseconcerns by presenting an innovative method that integrates theCPM-network developed in DES simulation environment with the SDmodel. The implementation infrastructure uses a discrete simulationengine, CPM network, and an SD simulation engine.

The proposedmethod has been illustrated using two real cases fromthe construction industry. A signicant difference in the outcomes be-tween the static and dynamic models has been observed. The projectcompletion duration has thus noted to increase signicantly from theplanned duration by considering the effects of the dynamic impact onthe project execution.

Although the proposed method provides practical steps and tools todevelop a dynamic schedule, yet there are certain limitations and possi-ble future enhancements to this hybrid system. For instance, the pro-posed method is based on the unidirectional interactions of variablesin DES and SD models. Bidirectional interactions of variables in simula-tion is has not been explored extensively yet. It is believed that simulta-neous interaction between the hybrid simulation modules would be ofgreat value in better understanding of project behaviors, as well as pro-ducing estimates that are more accurate. In addition, different hybridmodel structures need to be tested and investigated to conrm themethod's applicability and accuracy.

SD is not fully utilized in constructionmodeling despite the potentialbenets that it might bring to traditional planning methods. The cou-pling of traditional methods with SD is expected to provide a better un-derstanding of project mechanisms resulting in more realistic scheduleand cost estimates. Traditional modeling techniques supply detailed in-formation while the SD quanties the impacts of management policiesand strategies on project execution. The proposed hybrid method is ex-pected to enhance the current practices in project planning and model-ing, thus potentially beneting owners and clients worldwide. Betteraccounting of uncertainty and dynamics experienced during actualreal-time projects, is the main feature of the proposed hybrid modelingmethod.

Acknowledgment

The authors would like to acknowledge the nancial support of theNatural Sciences and Engineering Research Council of Canada (NSERC)

(Grant no. RGPIN/4430-2010) for the present research.

Please cite this article as: H. Alzraiee, et al., Dynamic planning of constru(2014), http://dx.doi.org/10.1016/j.autcon.2014.08.011[33] M. Marzouk, O. Moselhi, Object-oriented simulation model for earthmoving opera-tions, J. Constr. Eng. Manage. 129 (3) (2003) 173182.References

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