Modeling labor productivity in construction projects using ...scientiairanica.sharif.edu/article_4171_ee1dbfa3336395a6e64f38ba172f1450.pdf · Modeling labor productivity in construction

Scientia Iranica A (2017) 24(6), 2752{2761

Sharif University of TechnologyScientia Iranica

Transactions A: Civil Engineeringwww.scientiairanica.com

Modeling labor productivity in construction projectsusing hybrid SD-DES approach

S. Moradia, F. Nasirzadehb;� and F. Golkhooc

a. Department of Project and Construction Management, School of Art and Architecture, Tarbiat Modares University (TMU),Tehran, Iran.

b. Department of Civil Engineering, Faculty of Engineering, Payame Noor University (PNU), P.O. Box 19395-3697, Tehran, Iran.c. Department of Building, Civil & Environmental Engineering, Faculty of Engineering and Computer Science, Concordia

University, Montreal, Canada.

Received 5 November 2015; received in revised form 5 January 2016; accepted 10 October 2016

KEYWORDSProductivity;Constructionsimulation;Hybrid simulation;System dynamics;Discrete eventsimulation.

Abstract. Labor productivity is one of the most signi�cant factors in the evaluation of theperformance of construction projects. Improvement of labor productivity is believed to havedirect impact on outperformance of the project. This research argues about a hybrid SD-DES approach to model labor productivity considering the e�ects of both the context andoperational level factors. The complex inter-related structure of di�erent context factorsa�ecting the labor productivity is modeled using System Dynamics (SD) approach. DiscreteEvent Simulation (DES) is implemented to model the operational variables and their e�ectson labor productivity. Using the proposed hybrid SD-DES model, the labor productivitycan be determined more precisely since the e�ects of both context and operational variablesare taken into account. The proposed hybrid model is implemented in a real-world caseand the value of labor productivity is simulated considering the e�ects of both context andoperational variables.© 2017 Sharif University of Technology. All rights reserved.

1. Introduction

Economists and accountants consider productivity asthe ratio between total input of resources and totaloutput of product whilst project managers and con-struction professionals de�ne productivity as a ratiobetween earned work hours and expended work hours,or work hours used [1-3]. Since construction activitiesare normally labor-intensive, productivity is frequentlyreferred to as labor productivity [4].

Labor productivity is one of the most signi�cant

*. Corresponding author.E-mail addresses: [email protected] (S. Moradi);[email protected] (F. Nasirzadeh);[email protected] (F. Golkhoo)

doi: 10.24200/sci.2017.4171

parameters in the evaluation of the performance ofconstruction projects. Moreover, sometimes about50 percent of the total cost of project is consumedfor labor; thus, focus on increase in productivity canlead to a big save in overall costs [5,6]. Projectprocesses are highly a�ected by labor productivity dueto the dependence of cost estimating, scheduling, andoperation plan on the labor productivity. Produc-tivity can also play an important role in quality ofprojects; therefore, the management of labor and itsproductivity is crucial for determining the success of aconstruction project [1].

In construction, labor productivity is a�ected byboth context and operational parameters. Here, con-text represents the overall construction-related behav-ior (e.g., sta� skill level, collaboration level, motivation,inspection level, etc.), which dynamically interacts withoperations and changes as a result of the feedback

S. Moradi et al./Scientia Iranica, Transactions A: Civil Engineering 24 (2017) 2752{2761 2753

between the two. On the other hand, operationalfactors refer to process-related factors (e.g., sequenceof operations, number of resources, resource assignmentscheme of the processes, etc.), which a�ect the dura-tion of executing the process and, consequently, theproductivity. To model labor productivity consideringall the in uencing factors, both context and operationalparameters should be taken into account.

The productivity model of construction labor hasbeen studied since 1940 and this technique is stillbeing improved [7]. Zayed and Halpin [8] used linearregression to assess the productivity. Pan [9] estimatedthe impact of weather conditions on productivity andduration by a fuzzy based model. Song and AbuR-izk [4] proposed a systematic approach for measuringproductivity and collecting historical data and modeledproductivity by a combined neural network (ANN)and DES method. Watkins et al. [10] representedan agent based model to show the relationship be-tween congestion and labor e�ciency. Goodrum etal. [11] developed a model based on analysis of variance(ANOVA) and regression analysis to represent long-term improvements in labor productivity of activitiesdue to experiencing signi�cant changes in materialtechnology. Goodrum et al. [12] developed a fourstaged predictive model based on Analytic HierarchyProcess and historical analysis to estimate the impactof new technologies on productivity. Mawdesley andAl.Jibouri [13] conducted a survey to identify in uenc-ing factors on productivity and modeled their relativein uence on the productivity. Alvanchi et al. [14]introduced an approach for improving the productivityin construction projects by adjusting working hoursand developed a hybrid SD-DES model for estimat-ing expected productivity during di�erent hours ofovertime. Forcael et al. [15] studied productivitylevel during construction of transportation projects bysimulating di�erent bu�ering strategies in inventory,capacity, and time bu�ers. Nasirzadeh and Nojedehi [2]conducted a survey to identify in uencing factors onlabor productivity in construction and proposed aSystem Dynamics (SD) model to capture complexrelationships between identi�ed factors and estimateexpected labor productivity considering the e�ects ofidenti�ed factors.

The previous studies attempted to model thee�ects of one or some of the in uencing factors onthe labor productivity while, to have a comprehensivemeasurement of labor productivity, all a�ecting factorsshould be considered. Also, regarding the nature offactors in uencing productivity, they are not able toaccount for the e�ects of both context and operationalin uencing factors. It is therefore believed that noneof the previous research studies evaluate the value oflabor productivity accurately.

In order to cover the mentioned gap, this re-

search proposes a hybrid SD-DES approach to modellabor productivity considering the e�ects of both thecontext and operational factors. The complex inter-related structure of di�erent context factors a�ectingthe labor productivity is modeled using System Dy-namics (SD) approach. DES is implemented to modelthe operational variables and their e�ects on laborproductivity. The required steps to develop the hybridSD-DES model and synchronization of SD and DESmethods are illustrated. The proposed hybrid SD-DESmodel would be able to estimate uctuations of laborproductivity considering the variations of context andoperational in uencing factors simultaneously. Usingthe developed hybrid SD-DES model, consequences ofvarious managerial policies on labor productivity canbe evaluated more precisely. The �ndings are expectedto enhance understanding of the impact of variousinternal and external factors on the labor productivityperformance. Finally, the proposed hybrid model isimplemented in a real-world construction project andthe value of labor productivity as well as the e�ects ofdi�erent managerial policies is simulated consideringthe e�ects of both context and operational variables.

2. Research methodology

2.1. System dynamicsSystem Dynamics (SD), introduced by Forrester [16],is an objective-oriented simulation methodology en-abling us to model complex systems considering allthe in uencing factors [17-19]. Much of the art ofsystem dynamics modeling is discovering and repre-senting the feedback processes, which along with stockand ow structures, time delays, and nonlinearities,determine the dynamics of a system [20]. All systemsconsist of networks of positive and negative feedback,and all dynamics arise from the interactions of theseloops [20]. Negative loops are self-correcting and coun-teract change [20]; thus, they lead the system towarda balance. They are also called balancing feedback.Positive loops are reinforcing and make behavior of thesystem more disturbed [20].

Labor productivity in construction projects isin uenced by various factors that have complex in-teractions with each other. System dynamics canaccount for the complex inter-related structure of dif-ferent factors a�ecting labor productivity using causeand e�ect feedback loops [2]. Moreover, di�erentmanagerial policies that could be implemented for theimprovement of labor productivity could be modeledusing SD approach. As a result, the e�ectiveness ofmanagerial policies in improving labor productivitycould be assessed before they are actually implemented.

2.2. Discrete Event Simulation (DES)DES as a stochastic simulation tool can be used for

2754 S. Moradi et al./Scientia Iranica, Transactions A: Civil Engineering 24 (2017) 2752{2761

analyzing construction processes. DES considers theproject as a collection of construction processes; thus,by providing e�cient detailed operational information,the repetitive operations would be represented in astructured single process. Duration of each operationis estimated using the historical data gathered fromsimilar past operations. In construction, analysis ofprocesses is complex in nature as the output from oneoperation usually acts as the input to another opera-tion. It can therefore be estimated that how changes inthe value of input factors of the predecessor operationsa�ect the results of the following operations. Thereby,DES facilitates an e�cient reduction in schedule com-plexity by representing the repetitive operations ona single process structure as well as capturing otherdetailed information, such as resources [21].

DES models the behavior of the system as a setof networks and queues. The DES modeling approachtries to model system behavior as its state evolvesover time by following the system events, which arerecognized as the change initiators in the state of thesystem [22]. Simulation of a process using DES canprovide the value of productivity as well as the projectduration taking account of the values of di�erentoperations duration, waiting times and delays, resourceutilization, etc.

2.3. Hybrid SD-DES simulationIn the hybrid SD-DES modeling approach, capabilitiesof one model cover shortcomings of the other toprovide more accurate results for system analysis [22].To achieve more realistic assumptions, Discrete EventSimulation (DES) was used for modeling the opera-tional parts of the system and System Dynamics (SD)for modeling the nonoperational parts of the system(i.e., feedback); this combination is called the hybridSD-DES modeling approach [14,21-25]. Various typesof hybrid modelling format among hierarchical, phaseto phase, or integrated hybrid SD-DES format shouldbe selected. For more details on the mechanism ofthe interactions between DES and SD parts, pleaserefer to Moradi et al. [25]. After selection of thehybrid simulation format, the next step is to determinewhat should be exchanged between SD and DES.Formulation of synchronizing SD and DES models andessential interactions are done based on the principlesproposed by Pritsker et al. [26]. Pritsker addressedthree basic interactions in dealing with continuousand discrete variables synchronization: (1) A discretechange in a variable may cause a discrete change inother continuous variables; (2) A continuous changein a variable, by reaching a threshold, may cause adiscrete change in interacting variables; and (3) Adiscrete change in a variable may change the functionaldescription of a continuously changing variable. Theseprinciples can be traced in many construction system

Figure 1. Mechanism of data exchange in hybridSD-DES model.

modeling situations to de�ne SD and DES interactionpoints. Interaction points are the variables whosevalues are changed or in uenced by variables of theother model during hybrid simulation. Figure 1 showsthe mechanism of data exchange between SD and DESparts of the hybrid model. Interaction points in eachpart of hybrid model (i.e., SD and DES) exchange datain prede�ned formats. The value of interaction pointsin SD can therefore be mapped into DES as an input,and vice versa.

3. Model structure

A owchart representing di�erent stages of modelinglabor productivity using the proposed hybrid SD-DESapproach is shown in Figure 2. As it can be seen in

Figure 2. Flowchart of di�erent stages of modeling laborproductivity by the proposed hybrid SD-DES modelingapproach.


this �gure, the proposed hybrid model tries to simulatethe labor productivity considering all the in uencingfactors.

As mentioned before, labor productivity is af-fected by both context factors (e.g., sta� skill level,motivation) and operational factors (e.g., number ofresources, resource assignment scheme of the pro-cesses). Therefore, in the �rst step, all factors af-fecting labor productivity including both operationaland context variables are identi�ed and classi�ed.Then, System Dynamics (SD) as an object orientedsimulation methodology is used to capture dynamicsof context variables taking account of their complexinteractions. Discrete Event Simulation (DES) isutilized to analyze the e�ects of operational variableson labor productivity. The interaction points and dataexchange mechanism between SD and DES models areselected. A hybrid SD-DES modeling method is thendeveloped to integrate and synchronize SD and DES to

model labor productivity considering both the contextand operational variables. The labor productivity can�nally be simulated using the proposed hybrid model.

In the following section, di�erent phases of theproposed hybrid model will be explained in detail.System Dynamics (SD) and Discrete Event Simulation(DES) as the most popular traditional simulation toolsare introduced and the need for a comprehensivehybrid model to integrate these simulation methods isdiscussed.

3.1. Identi�cation of in uencing factors onlabor productivity

In this research, labor productivity is de�ned as theratio of consumed man hours to work done. The laborproductivity is a�ected by various factors. There aresome previous studies that attempted to identify thesein uencing factors. Table 1 depicts di�erent in uencingfactors identi�ed by the previous studies [27-32].

Table 1. Di�erent factors a�ecting the labor productivity.

Authors The factors a�ecting labor productivity

Lim and Alum (1995)Lack of quali�ed supervisors, shortage of skilled labor, high rate oflabor turnover, labor absenteeism, and communications with foreignlaborers.

Zakeri et al. (1996)Materials shortage, weather and site conditions, equipment breakdown,drawing de�ciencies/change orders, and lack of proper tools andequipment.

Kaming et al. (1997) Lack of material, lack of equipment, interference, absenteeism,supervision delays, and rework.

Sonmez and Rowings (1998)Percentage of overtime work, crew size, work quantity, job type,percentage of laborers, work characteristics, and factors in equipmentcomponent.

Hanna et al. (1999)Order changes, work sequencing, work shifting, schedule compression,overtime work, absenteeism and turnover, labor problems, tradestacking, and material problems.

Enshassi et al. (2007)

Material shortages, lack of labor experience, lack of labor surveillance,misunderstandings between team members, work change orders,payment delays, labor disloyalty, inspection delay, working withouttaking holidays, tool and equipment shortages, rework, misuse of timeschedule, accidents, labor dissatisfaction, and supervisors' absenteeism.

Jarkas and Bitar (2012)

Clarity of technical speci�cations, the extent of variation/changeorders during execution, coordination level among design disciplines,lack of labor supervision, proportion of work subcontracted, designcomplexity level, lack of incentive scheme, lack of constructionmanager's leadership, stringent inspection by the \engineer", and delayin responding to \Requests For Information" (RFI).


Table 2. Di�erent operational and context factors a�ecting labor productivity.

Type In uencing factors

Context (continuous)

Supervision, weather conditions, job complexity, lack ofworking area, labor experience (skillfulness), motivation,project management e�ciency, fatigue, and familiarity withjob type

Operational (discrete) Overtime, rework, crew size, work quantity, lack of availablematerial, change order, and lack of equipment

Labor productivity is a�ected by both contextand operational parameters. Here, context is referredto by factors with a continuous behavior over time andincludes context factors such as weather conditions,fatigue, learning curve, etc. Context variables havecomplex inter-relationships that can reinforce theire�ects on the value of labor productivity. The complexinter-related structure of di�erent context parameterscan be captured by a system dynamics model that willbe introduced in the next section.

In contrast, operational parameters are indicantsof process related factors such as sequence of opera-tions, number of resources, resource assignment schemeof the processes, etc., which a�ect the duration of exe-cuting the process and, consequently, the productivity.DES can be implemented to model the operationalvariables and their e�ects on labor productivity. Theprevious studies did not account for both the discreteand continuous parameters (operational and contextvariables).

In this study, a questionnaire survey was con-ducted with 80 experts and practitioners to identifydi�erent factors a�ecting labor productivity in con-struction projects. 26 responses were received, of which5 were not applicable. Table 2 depicts di�erent factorsa�ecting labor productivity. As shown in Table 2,the in uencing factors have been classi�ed into twogroups. The variables that are related to operationallevel, including overtime, rework, crew size, workquantity, lack of available material, change order, andlack of equipment, are considered as discrete factors.The context parameters such as supervision, weatherconditions, job complexity, lack of working area, laborexperience (skillfulness), motivation, project manage-ment e�ciency, fatigue, and familiarity with job typeare categorized as continuous factors.

3.2. Hybrid model of in uencing factors onlabor productivity

The e�ects of di�erent continuous factors a�ecting la-bor productivity can be captured by System Dynamics(SD). However, it can lead to unrealistic representa-tion of discrete construction operations [23]. On theother hand, Discrete Event Simulation (DES), whichhas been introduced as the most suitable tool for

modeling of project processes, has a limited ability tomodel the complex inter-related structure of continuousin uencing factors due to its discrete event-orientednature. Compared with DES's emphasis on inputrandomness, SD's emphasis on the system structure(i.e., feedback process) bolsters the understanding ofthe system [21]. It is believed that more accurateresults in productivity estimation would be obtainedwhen operation and context are taken into accountsimultaneously for a comprehensive understanding oflarge-scale construction [21].

The operational parameters such as work inprocess, number of workers, and work done will becalculated in the DES part of the model. On the otherhand, continuous variables such as productivity andrequired time to complete the process are determinedin SD part of the model and can be sent to the DESpart as input. The hybrid model structure is explainedin more detail in the case example presented below.

4. Model application: Simulation of laborproductivity in a concreting process

The proposed hybrid SD-DES model was implementedin a real construction project to simulate the laborproductivity considering the e�ects of both contextsand operational in uencing factors. This project caseexample is related to a concreting process consistingof three main operations. Data used for developingSD and DES models were collected from the realconstruction site.

4.1. Case descriptionThe concreting process consists of three main op-erations, namely, concrete pouring, vibrating, and�nishing works. A resource pool of workers is allo-cated for each operation. Two pumping workers, onevibrator, and one �nisher are allocated for concretepouring, vibrating, and �nishing works, respectively.The workday length is eight hours.

4.2. System Dynamics (SD) simulationmodeling of the labor productivity

As the �rst phase of developing the proposed hybridSD-DES model, the in uencing factors on labor pro-ductivity were identi�ed and classi�ed into discrete


Figure 3. SD model of the labor productivity (adopted from [2]).

variables (operation) and continuous variables (con-text) as shown in Table 2. The discrete and continuousvariables could be modeled by DES and SD simulationschemes, respectively.

Figure 3 depicts the SD model of labor productiv-ity. As shown in this �gure, the labor productivity isa�ected by various context in uencing factors. The SDmodel has been adopted from [2] and slightly modi�edto be used in the proposed hybrid modeling structure.

As shown in Figure 3, the labor productivity isa�ected by various factors negatively and positively.Some factors such as skillfulness, worker's motivation,and project planning and management a�ect laborproductivity positively. However, there exist someother factors such as fatigue, lack of working area,and lack of available material that a�ect labor pro-ductivity negatively. The complex interactions thatexist between these factors have also been shown inFigure 3. For example, project planning and manage-ment that a�ects labor productivity is in uenced bytwo factors, including reference project planning andcurrent project planning.

4.3. Discrete Event Simulation (DES)modeling of the labor productivity

The labor productivity is in uenced by operationalvariables (discrete variables) in addition to the contextvariables. These operational variables are modeled inDES part of the proposed hybrid model. Figure 4represents the DES model of concreting process. Theconcreting process consists of three operations includ-ing pouring, vibrating, and �nishing works (Figure 4).

In the DES model, each cubic meter of concreteis considered as an entity. The capacity of trucks thattransport concrete from batching plant to the site isconsidered to be 7 m3. It is obvious that while onetruck has not �nished pouring operation, the othertrucks that reach the site afterwards must stay in aqueue. In addition, when pouring operation is delayed,the workers allocated to the two subsequent tasks,i.e. vibrating and �nishing, will have no work to do.To develop DES model of the concreting process, therequired data were gathered from a real concretingproject and probability distributions of duration ofoperations were determined. Finally, duration of the

Figure 4. DES model of a concreting process.


process, resource utilization, work in process, andvolume of concreting (work done) were calculated byDES model.

4.4. Hybrid SD-DES modeling of the laborproductivity

Having constructed the SD and DES models, thehybrid model of labor productivity can be developed.For this purpose, the interaction points and the dataexchange mechanism should be de�ned. Table 3 showsthe interaction points between SD and DES models.Variables such as \rate of doing work" are calculatedin the SD model and act as inputs to the DES partof hybrid model. On the other hand, variables such as\work done" and \number of workers" are determinedin DES model and act as inputs to the SD part ofhybrid model.

In the SD model, productivity is simulated as acontinuous variable considering all the context in u-encing factors (Figure 3). The simulated value of laborproductivity a�ects the rate of doing work, which actsas an input to the DES model. In the DES model(Figure 4), the duration of operations is determinedbased on the rate of doing work.

4.5. Results and discussionThe proposed hybrid model was implemented in a real-world construction project. In the following section,the simulated values of labor productivity as well asthe e�ects of di�erent in uential factors are presented.Using the proposed hybrid SD-DES model, the valueof labor productivity can be predicted consideringthe values of various in uencing factors. Table 4shows predicted values of labor productivity for 4di�erent cases. The four cases were selected to revealcapabilities of the proposed hybrid simulation model inpredicting the labor productivity considering the valuesof di�erent context and operational level in uencing

Table 3. The interaction points between SD and DESmodels.

From SD to DES From DES to SD

Rate of doing work Work doneNumber of workers

factors. According to data from case study, in normalcondition (base case), the time required for completingconcreting operation is 0.2 hour for each cubic meterof concrete. Therefore, the normal productivity can beconsidered to be 5 m3 per hour. In cases of no. 1 to3, the value of labor productivity gradually decreasesfrom the base case value of 5m3 per hour. The reasonis that the skillfulness and the working area decreaseand the weather temperature increases in these cases.

Figure 5 shows the variations of the processcompletion time of each entity against di�erent valuesof working area index. Working area index is de�ned asthe ratio of the available working area to the requiredworking area. As shown, the value of working areaindex varies between 1 and 0, where 1 represents thecase in which the available working area is equal to therequired working area. When the number of workersexceeds a speci�ed threshold, the value of working areaindex decreases from 1 and the labor productivity willdecline and, consequently, the process completion timeof each entity will increase.

As shown in Figure 5, in normal condition thatthe working area index is equal to 1, the correspondingduration is estimated to be 0.2 by the proposed hybridmodel. When the working area index decreases from 1to 0.1, the process completion time of each entity willincrease from 0.2 to 0.6 hour. Obviously, simulationof labor productivity by a single DES model leads tounrealistic results since the interference of working crewarising from the lack of working area is not taken intoaccount.

Figure 5. Process completion time of each entity vs.di�erent values of working area index.

Table 4. The simulated values of labor productivity achieved by the proposed hybrid SD-DES model.

Case number SkillfulnessSite

temperature(�C)

Workingarea

Number ofworkers

Processcompletion time

of eachentity (hour)

Productivity(m3/h)

Base 1 20 1 4 0.2 51 0.9 26 0.9 8 0.26 3.82 0.8 32 0.7 9 0.38 2.63 0.6 40 0.6 10 0.76 1.3


Figure 6. E�ect of site temperature uctuations onprocess completion time.

Figure 7. E�ect of labor skillfulness on the processcompletion time.

In the previous section, weather condition wasintroduced as another important factor a�ecting thevalue of labor productivity. In this research, weathercondition is regarded as the site temperature thatvaries during di�erent hours of workday. Figure 6represents the e�ect of site temperature uctuationson process completion time of each entity.

As shown in Figure 6, the process completion timeof each entity increases from 0.2 hour to 0.23 hour,corresponding to the site temperatures of 20�C and40�C, respectively.

Labor skillfulness has been introduced as anothercontext variable a�ecting the value of labor produc-tivity and the process completion time of each entity.Skillfulness is determined based on the ratio of thecurrent work experiences of the labor to the minimumrequired work experiences [2]. Figure 7 represents thee�ect of labor skillfulness on the process completiontime of each entity.

As shown, when skillfulness is equal to 1, theprocess completion time of each entity corresponds to0.2 hour. As the skillfulness decreases from 1 to 0.1,the process completion time of each entity escalates andreaches a maximum value of 0.3 hour.

Finally, the performance of the proposed hybridSD-DES model is compared with a DES simulationmodel and the real case results of a concreting op-eration. As shown in Table 5, using a single DESmodel, the value of work done is predicted as 154 m3 ofconcrete pouring. However, using the proposed hybridSD-DES model, it is revealed that the produced resultsof DES are over-estimated and the value of work done

Table 5. Comparison between the simulated valuesachieved by DES and the proposed hybrid SD-DES model.

DESProposed

hybridSD-DES

Realcase

Productivity ofconcreting operation (m3

in an 8-hour workday)154 147 145

would be limited to 147 m3 of concrete pouring, whichis more realistic than the performance achieved by realcase outputs. It is believed that using the proposedhybrid SD-DES model, the value of labor productivitycan be simulated more precisely since both the contextand operational level in uencing factors are taken intoaccount.

5. Conclusions and remarks

A hybrid SD-DES approach was presented to simulatethe value of labor productivity considering the e�ectsof both continuous (context) and discrete (operational)in uencing variables. System Dynamics (SD) approachaccounted for the highly dynamic complex inter-relatedstructure of di�erent context factors. On the otherhand, Discrete Event Simulation (DES) was used forsimulating the work processes and operational in uenc-ing factors. The required steps to develop the hybridSD-DES model and synchronize SD and DES methodswere illustrated.

The applicability and performance of the pro-posed hybrid model were assessed by implementing itin a real world project. It was shown that how the valueof labor productivity could be predicted considering allthe context and operational level in uencing factors. Asensitivity analysis was �nally conducted to evaluatethe e�ect of di�erent in uencing factors on laborproductivity. The results of the sensitivity analysisrevealed that how the labor productivity was a�ectedby di�erent in uencing factors such as working area,weather temperature, and skillfulness. The proposedhybrid SD-DES method may present a novel and robustapproach to simulate the value of labor productivitysince the e�ects of both context and operational levelin uencing factors are taken into account.

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Biographies

Saeed Moradi received his MSc degree in Projectand Construction Management from Tarbiat ModaresUniversity (TMU), Tehran, Iran, in 2012. He is cur-rently PhD student at Concordia University, Montreal,Canada. His research interests include labor pro-ductivity in construction, simulation of constructionprocesses, and infrastructure management.

Farnad Nasirzadeh received his MSc and PhDdegrees in Construction Engineering and Manage-ment from Iran University of Science and Technology,Tehran, Iran. He is currently an Associate Professorin the Department of Civil Engineering at PayameNoor University (PNU). His research interests includeproject risk management and modeling and simulationof construction projects.

Farzaneh Golkhoo received her MSc degree inProject and Construction Management from TarbiatModares University (TMU), Tehran, Iran, in 2012.She is currently PhD student at Concordia University,Montreal, Canada. Her research interests includeconstruction process simulation, construction 3D mod-eling, and material management.

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