施工仿真 (141)

Integrated Simulation System for Construction Operationand Project SchedulingDong-Eun Lee,A.M.ASCE1;Chang-Yong Yi2;Tae-Kyung Lim3;and David Arditi,M.ASCE4 Abstract:Simulation modeling is important in predicting the productivity of construction operations and the performance of project schedules.It would be desirable if operation and project models are vertically integrated in practice.However,existing discrete event simulation systems do not allow integrating operation and project models.This paper introduces an integrated simulation system named “Construction Operation and Project Scheduling”͑COPS͒.COPS analyzes the productivity of construction operations as well as the performance of a project schedule individually and jointly.It creates operation models,maintains these models in its operation model library,conducts sensitivity analysis with different resource combinations,finds the optimal resource combination that satisfies the user’s requirements relative to hourly production and hourly cost of the operation,feeds this information into a project schedule,and executes stochastic simulation-based scheduling.A case study is presented to demonstrate this integrated simulation system.DOI:10.1061/͑ASCE͒CP.1943-5487.0000061CE Database subject headings:Construction management;Models;Scheduling;Simulation;Productivity;Case studies.Author keywords:Construction operation model;Schedule model;Discrete event simulation;Simulation based scheduling.IntroductionA project schedule is modeled by establishing the relationships among activities,which in turn are formulated by making use of construction operations.Hence construction activities consist of individual operations and are interconnected to describe a project ͑Halpin and Riggs1992͒.Operations research-based discrete event simulation͑DES͒systems are effectively used to model construction processes and to analyze their productivity at the operation level.On the other hand,DES-based scheduling systems also model the uncertainty of activity durations and effectively handle the variability of PCTs at the project level.However,these systems are not used exten-sively in practice because of various deficiencies that hamper their usability.What is more,no attempt was ever made to reap the benefits of integrating operation-level and project-level sched-uling models.This study presents an integrated simulation system called Construction Operation and Project Scheduling͑COPS͒,an automated tool that allows the mapping of operation models into a project schedule.The research activities were conducted in four steps.First,a strategy was set to eliminate the deficiencies of the existing sys-tems both at the operation and project levels.Second,an entirely automated system that accommodates the strategy was developed. Third,a detailed demonstration of the new system was illustrated using small operation models and a project schedule.Fourth,the capability of the system to integrate network models of different levels and to handle a large project schedule was verified.The contents of this paper are organized in the same order. Current State of Existing Simulation Systemsof Operation and Project SchedulesDES-based systems at the operation and project levels are inde-pendently used to improve operation productivity and projectscheduling performance,respectively.These systems are well ac-cepted in a wide range of applications in practices.After Halpin͑1977͒introduced CYCLONE,thefirst DES-based system specializing in construction operations,manyresearchers developed various DES-based systems to model con-struction operations and to predict and improve operation produc-tivity.Examples of these systems include UM-CYCLONE ͑Ioannou1990͒that allows loops to establish a cyclic network; CRIPROS͑Tommelein et al.1994͒that defines resource proper-ties,design component properties,and the relationships betweentasks effectively;STROBOSCOPE͑Martinez1996͒that effi-ciently runs complex operation models;SIMPHONY͑AbouRizkand Mohamed2000͒that models various construction resourcesand theirflows using icons;DISCO͑Huang and Halpin1995͒andCOOPS͑Liu1995͒that improve CYCLONE by converting it intoa computer graphic environment;ABC͑Shi1999͒that simplifiesmodeling by reducing the number of design components toactivity-on-nodelike design components;extended version ofABC͑Hong et al.2002͒that improves the usability of ABC͑Shi1Assistant Professor,School of Architecture and Civil Engineering, KyungPook National Univ.,1370,Sangyegk-Dong,Buk-Gu,DaeGu, 702-701,Korea.E-mail:dolee@knu.ac.kr2Graduate Student,School of Architecture and Civil Engineering, KyungPook National Univ.,1370,Sangyegk-Dong,Buk-Gu,DaeGu, 702-701,Korea.3Graduate Student,School of Architecture and Civil Engineering, KyungPook National Univ.,1370,Sangyegk-Dong,Buk-Gu,DaeGu, 702-701,Korea.4Professor,Dept.of Civil and Architectural Engineering,Illinois In-stitute of Technology,Chicago,IL60616͑corresponding author͒.E-mail: arditi@Note.This manuscript was submitted on July15,2009;approved on March26,2010;published online on March29,2010.Discussion period open until April1,2011;separate discussions must be submitted for individual papers.This paper is part of the Journal of Computing in Civil Engineering,V ol.24,No.6,November1,2010.©ASCE,ISSN 0887-3801/2010/6-557–569/$25.00.1999͒by incorporating two-dimensional animation capability;and case-based reasoning͑Graham et al.2004͒and fuzzy͑Chengand Wu2006͒systems that improve the practicality of modelingtask durations.These systems have been under rigorous testing toverify their applicability to various construction operations suchas earthmoving͑CYCLONE,Halpin1977͒,constructing a con-crete structure͑CIPROS,Tommelein et al.1994͒,building anairport service center͑STROBOSCOPE,Martinez and Ioannou1994͒,installing precast concrete components͑COOPS,Liu1995͒,placing concrete͑Micro CYCLONE,Alkoc and Erbatur1997͒,constructingfloating caissons͑PROSIDYC,Halpin andMartinez1999͒,driving piles͑Micro CYCLONE,Zayed andHalpin2001͒,and redecking a bridge͑Cell-DEVS,Micro CY-CLONE,Hong et al.2006͒,to name but a few.Defining the probability distribution function of activity dura-tion or project completion time has been the subject of a varietyof research projects.For example,AbouRizk et al.͑1991͒presenta procedure that estimates the␤-shaped parameters͑␣and␤͒using a public software system called VIBES.AbouRizk and Hal-pin͑1992͒demonstrated that most earthmoving construction op-erations can be described by the␤probability-density function ͑PDF͒.Farid and Koning͑1994͒confirmed that the␤PDF is the closestfit for modeling construction task time distributions.Schexnayder et al.͑2005͒also confirmed the usability of the␤PDF.Maio and Schexnayder͑2000͒and Fente et al.͑2000͒de-termined the parameters of a␤PDF by extending AbouRizk andHalpin’s work͑AbouRizk and Halpin1992͒.These methods are particularly useful when not enough data are available or when subjective estimates are involved.However,there are efficient IT tools nowadays͓e.g.,radio frequency identification͑RFID͒,ubiq-uitous sensor network͑USN͒,and ZigBee͔that can be used to collect large quantities of historical task duration data.Therefore, a tool that identifies the best-fit-PDFs and that computes their parameters based on historical task durations would be most de-sirable in the context of COPS.Such a tool would improve the reliability and accuracy of the system.Existing DES-based systems have been widely used to model construction operations and to analyze operation productivity. These systems model cyclic networks using tasks as the atomic building blocks and compute hourly production rates,operation completion times͑OCTs͒,utilization of input resources,and op-eration completion costs͑OCC͒.In addition,sensitivity analysis is used to identify the best resource combination that maximizes productivity and satisfies job site constraints͑Halpin and Riggs 1992͒.However,at the operation level,the existing DES-based systems have several shortcomings relative to their usability.First,most existing systems depend on commercial statistics software packages͑e.g.,SPSS,SAS,and Crystal Ball͒.One can estimate the PDFs of task durations by manually and indepen-dently operating the software packages and manually assigning the PDFs of the task durations to the time delay functions of tasks in the model.This inconvenience is more acute when one deals with large and/or complex networks.Second,existing systems use two rules to decide when to stop the simulation.One involves terminating the simulation when the resourceflow unit͑e.g.,truck͒reaches an arbitrarily predefined number of cycles͑e.g.,30cycles͒.The other is terminating the simulation when the time arbitrarily predefined by the system user ͑e.g.,10,000s͒is exhausted.While some existing systems allow inputting a user-defined value using a graphical user interface ͑GUI͒,some others allow customizing the termination conditions by using extension languages or scripts.But it is always question-able whether the simulation experiment is terminated before or after it has reached maturity.Yet the accuracy of simulation out-puts relies on the number of simulation runs.Existing systems arelimited in that they treat the simulation output͑e.g.,estimates oftotal OCT,hourly production rate,utilization efficiency of inputresource,and total OCC͒as“true”model characteristics eventhough these estimates are just particular realizations of randomvariables that may have a large variance.Third,existing systems require that the output data obtainedby performing sensitivity analysis be exported to external appli-cations͑e.g.,Excel͒because these data need to be manually ma-nipulated to identify a set of optimal resource combinations thatsatisfy operation-specific constraints͑e.g.,total operation timeand cost͒.Therefore,obtaining the optimal solution is a lengthyand cumbersome process.This inconvenience is exacerbatedwhen the system is used on a large operation network consistingof many resource combinations.Whenever DES-based operation analysis systems are extendedto the project schedulingfield͓i.e.,CYCLONE-CPM͑Halpin1990͔;Stroboscope’s CPM Add-On͑Martinez and Ioannou1997͒;Sim-Con͑Chehayeb and AbouRizk1998͒;ABC-CPM ͑Shi1999͔͒,it is observed that the new systems inherit the defi-ciencies of their predecessors.However,there are other DES-based systems that achieve acceptable levels of predictability andthat establish an optimal resource input strategy by efficientlydealing with the uncertainty of activity durations͑or costs͒andthe variability of project completion times͑PCTs͒͑Feng and Liu2000;Barraza et al.2004;Lee and Arditi2006͒.These systems model stochastic noncyclic networks using activities as their atomic building blocks.However,these systems also have several deficiencies.First,these systems also depend on commercial statistics soft-ware packages͑e.g.,SPSS,SAS,and Crystal Ball͒.Therefore, they are also quite inconvenient in dealing with large networks.In addition,they depend on the availability of historical activity du-ration data.Second,the output of systems running at the operation level is not used by a system running at the project level.It is true that there are systems that link operation models to each other such as CIPROS͑Tommelein et al.1994͒and Sim-Con͑Chehayeb and AbouRizk1998͒,but these systems are not capable of integrating different systems operating at different levels,hence preventing the information obtained from operation models from being used in project scheduling.Some researchers proposed similar integrated models a decade ago in the names of“hierarchical,”“modular,”and“multilevel”project scheduling.The concepts were inspired by simulation building blocks proposed by Sawhney and AbouRizk͑1996͒and introduced by AbouRizk and Mather͑2000͒to the construction simulation community.The hierarchical and modular modeling capability was promoted as an effective method to model com-plex,multilevel,and large projects.These concepts were built into a program called Symphony͑Hajjar and AbouRizk1999, 2002͒and were accepted as industry standards by the developers of general purpose simulation software͑e.g.,ARENA;Kelton et al.2007͒.However,any benefits of this type of integration have not been realized especially for large project schedules that have hundreds or thousands of activities because these systems inherit the limitations of operational research oriented DES sys-tems.That is why,a new system which bridges the gap between operation simulation and project scheduling methodologies is justified.It is clear that most existing systems do not have the built-in capability tofind the best-fit-PDFs of the many tasks;to assignthe best-fit-PDFs obtained from operation models to respective activity durations in a project schedule;to verify if simulation has reached maturity;and to find the best-fit-PDF of PCTs.Succinctly said,they are not capable of injecting operation information into project scheduling and they cannot handle large project schedules such as those frequently encountered in practice.MethodologyThe proposed system called COPS automatically estimates the best-fit-PDFs of historical task durations,runs the operation mod-els for an appropriate number of simulation cycles,estimates the PDFs of OCTs and costs ͑OCCs ͒,assigns the PDFs of completion times and costs to the respective activities of a schedule,gener-ates random variates using the individual PDFs assigned to each activity,runs CPM for an appropriate number of iterations,esti-mates the PDF of PCTs and costs ͑PCCs ͒,and makes a prediction relative to project completion time and cost.In this way,COPS supplies information obtained at operation level to a scheduling model at project level.The system is capable of synthesizing the productivity information obtained from the DES-based operation model and the performance information obtained from the DES-based project scheduling model.It covers analysis and scheduling at both the operation and project levels.It was developed by using MATLAB ͑MathWorks 2007a ͒for programming and SimEvent͑MathWorks 2007b ͒for simulation.The detailed description of the DES-based operation analysis and project schedule modules of COPS is provided in the following sections.DES-Based Operation Analysis ModuleThis module consists of:͑1͒operation model building;͑2͒re-source initialization;͑3͒simulation execution;and ͑4͒simulation output analysis,as shown in Fig.1.The 13-step algorithm is presented in the following paragraphs.Operation Model Building•Step 1:the resources allocated to a construction operation are identified.Then,the tasks to carry out the operation are de-fined.A construction operation model is established by using the model building blocks shown in Fig.2.The active and passive states of the resources are distinctly identified and de-fined in a process.This is of great importance in productivity measurement.A static construction operation model is estab-lished by combining the processes that make use of the same resources.•Step 2:the historical task duration data stored in spreadsheet format ͑Section “A”in Fig.3͒are read by the system.Then,the best-fit-PDFs and their parameters are computed using the automated best fit algorithm integrated into the system ͑Lee et al.2009͒.This information is stored in Section “B”in Fig.3.The system also allows defining the PDF manually by the system user as done by most existing DES-based operation systems.The flowchart in Fig.4presents the algorithm relative to how the best-fit-PDFs and their parameters ͑␳1,␳2,␳3,␳4͒are computed by COPS.Historical activity duration data stored in a database file are read in step Ꭽ.The user selects one of two statistical hypothesis testing methods in step Ꭾ:͑1͒the likelihood ratio test,or ͑2͒the chi-square test depend-ing on the user’s preference.If the user selects the likelihood ratio test,the algorithm that estimates the best-fit-PDF pro-ceeds to step Ꭿwhere the goodness-of-fit of the PDFs is cal-culated by computing the negative log-likelihood value.The parameters of each PDF are estimated by using maximum like-lihood estimators ͑MLEs ͒in step ൳.The PDF having the minimum negative log-likelihood value is selected as the best-fit-PDF in step ൴.If the user selects the chi-square test,theFig.1.Algorithm of DES-based operation analysismoduleFig.2.Model building blocks of the DES-based operation analysis modulealgorithm proceeds to step ൵where the goodness-of-fit of the PDFs is calculated by computing p -values.Then,the param-eters of each PDF are estimated in step ൶by using MLEs.The PDF having the maximum p -value is selected in step ൷as the best-fit-PDF.A total of 21types of distribution are available in MATLAB and are included in the distribution fitting tool.Other types of distribution can be accommodated easily into the tool upon adding them into MATLAB.The best-fit-PDFs of a set of historical task durations and their parameters were computed using the algorithm in Fig.4.The random variates of task durations were generated using the random number generator that worked within the confines of the best-fit-PDFs and their parameters identified by the algorithm.The validity of the algorithm that estimates the best-fit-PDF in Fig.4was confirmed after comparing the PDFs and their parameters gen-erated by this method with those generated by a commercial statistical package called Minitab 14.It was verified that the two outcomes were statistically the same by using the Stu-dent’s t -test at a 95%confidence interval ͑Wilcox 1997͒.Resource Initialization•Step 3:the number of resources used in an operation model are defined in a certain range using two point estimates,maxi-mum and minimum values.Simulation experiments are ex-ecuted for all possible resource combinations by performing sensitivity analysis.The sensitivity of the production system isinvestigated to determine the most appropriate number of input resources.The OCTs and OCCs of each resource com-bination are computed and saved in a matrix.•Step 4:the optimal resource combination is identified.This resource combination meets all constraints specific to the job site ͑i.e.,activity duration and cost ͒either committed by or commissioned to a subcontractor.The flow units in the optimal resource combination are initialized in appropriate initial loca-tions to determine the model’s response.Execution of Operation Network Simulation•Step 5:by default,the system sets the number of simulation iterations to 120assuming a 99%confidence level ͑Lee and Arditi 2006͒.The iteration counter is set to zero.•Step 6:task durations are generated using the random number generator which produces random variates following the best-fit-PDFs and their parameters ͑refer to Section B in Fig.3͒assigned to tasks’time delay functions in Step 2.OCTs are obtained after conducting simulation runs of the operation model that incorporates the task durations.Since the random variates of task durations are different in every simulation run,they contribute to the variability of OCTs as well.•Step 7:the simulation output data ͑i.e.,OCTs,OCCs,and hourly productions ͒obtained in Step 6are saved in a matrix.When 120simulation iterations are completed,120sets of OCTs,OCCs,and hourly productions are obtained and saved in respective matrices.•Step 8:the algorithm repeats Steps 6to 8,until the total num-ber of simulation runs reaches 120.•Step 9:the minimum number of simulation runs is calculated using the set of 120OCTs ͑Ang and Tang 1975;Lee and Arditi 2006͒.•Step 10:the system checks whether the simulation experiment passes the maturity test,i.e.,whether more than 120iterations are necessary to ensure the reliability of the simulation experi-ment ͑Crandall 1977;Bennett 2001͒.The algorithm checks if the minimum number of simulation runs calculated in Step 9is greater than 120iterations set by the system at the outset in Step 5.If the minimum number of simulation runs is equal to or smaller than 120,the simulation experiment is considered to have reached appropriate maturity ͑Lee and Arditi 2006͒.Oth-erwise,the system’s workspace is cleared,the maximum num-ber of simulation iterations is set to the value calculated in Step 9,and the algorithm returns to Step 6.Then,Steps 6to 10are repeated.Analysis of the Operation Network Simulation Output•Step 11:when the simulation experiments reach maturity,the best-fit-PDF and its parameters describing the simulation out-put data ͑i.e.,OCTs,OCCs,and hourly productions saved inaFig.3.Historical task durationtableFig.4.Algorithm for estimating the best-fit-PDFmatrix in Step 7͒are calculated by using the automated distri-bution fitting tools.Then they are saved in the computer’s memory.•Step 12:the user may query the probability to complete the construction operation within a specific deadline using a dia-log box prompted by the system.•Step 13:the system provides the probability to complete the operation within the deadline queried in Step 12.DESB Project Scheduling ModuleThis module models a project schedule network and analyzes its performance.It consists of:͑1͒schedule network modeling;͑2͒simulation execution;and ͑3͒simulation output analysis,as shown in Fig.5.A schedule network can be modeled and ana-lyzed only if operation models are well established and main-tained in the operation model library.The network model building blocks ͑or design components ͒of the module are presented in Fig.6.The algorithm is presented in Fig.5.Schedule Network Modeling•Step 1:a schedule table that displays schedule information including the relationships among activities is either created by using the model building blocks of the project schedule module ͑Fig.6͒or is exported from P3using its built-in export function.The finish-to-start relationship is used in CPM com-putation because according to Lu et al.͑2008͒,nonfinish-to-start relationships with lag times ͑e.g.,start-to-start,finish-to-finish ͒are rare in construction project networks and are likely to contain implicit resource constraints.Thus,Lu et al.͑2008͒adviced that nonfinish-to-start relationships be transformed into finish-to-start relationships prior to resource constrained scheduling analysis.•Step 2:The operation models that were stored and maintained in the operation model library,are mapped to the activities of the schedule network.This can be done by nesting operation models into activities using dragging-and-dropping manipula-tion.It can also be done in a spreadsheet environment by as-signing operation models to each activity in the schedule table.The operation model ID is the primary key of the operation model library,but a foreign key in the schedule table.The relationship between the operation model library and the schedule table is of the one-to-many type,implying that the same operation model can be used by many activities of a schedule network.This option is particularly convenient when dealing with a large network,because it allows rapidly assign-ing many operation models to many activities in a spreadsheet environment.Regardless of which option is used,this approach eliminates the dependency of project scheduling on the availability of his-torical activity duration data,since OCTs generated by operation models play the role of historical activity duration data.The sys-tem assigns the best-fit-PDFs of OCTs estimated in Step 11of the operation analysis ͑Fig.1͒to activity durations,and uses the ac-tivity durations provided by the random number generator that made use of these best-fit-PDFs and their parameters.Schedule Network Simulation Execution and Output Data Analysis Modes•Steps 3to 11:these steps are identical to Steps 5to 13of the operation analysis in Fig.1discussed previously,except that a schedule network is used instead of an operation network and historical activity duration data are replaced by the best-fit-PDF of OCTs obtained in the operation analysis.PCTs consti-tute the output of the simulation.Since the random variates and activity durations are different in every simulation run,they contribute to the variability of the PCT.Operation disturbances,which are defined as unexpected oc-currences causing an interruption or a delay in the execution ofFig.5.Algorithm of DES-based project schedule moduleFig.6.Model building blocks of the DES-based project scheduling moduletasks and causing a significant discrepancy between the target andactual productivity,are very important phenomena to be consid-ered in a simulation study.Construction operations are sometimesinfluenced by external unforeseen events such as weather condi-tions,soil characteristics,space availability,traffic conditions,ac-cidents,and equipment breakdowns.Construction operations arealso sometimes influenced by internal problems such as poor de-cision making,communication problems,reworks caused by de-sign changes initiated by the owner,and design errors caused bypoorly qualified personnel and/or subcontractors.Halpin andRiggs͑1992,p.69͒mention the typical operational disturbancesto be considered in the method productivity delay model ͑MPDM͒͑Adrian1974͒and production cycle delay sampling ͑Adrian1974͒.Because the overall assessment of disturbances in construction operations leads to a more realistic and accurate pro-ductivity prediction,it would be desirable to incorporate the dy-namic evolution of disturbances into construction operationsimulation.Several researchers provide methods which allow in-corporating construction operation disturbances into constructionoperation simulation to achieve more realistic simulation of con-struction processes.For example,Gehbauer et al.͑2007͒alloweddisturbances to be parameterized and to be taken into account inconstruction operation models.Lee and Pena-Mora͑2007͒pro-posed a hybrid system that takes into account the dynamic evo-lution of operational disturbances and that computes the scheduledelay attributed to disturbances.Motawa et al.͑2007͒incorpo-rated fuzzy logic into simulation to predict the impact of distur-bances.Anderson et al.͑2009͒incorporated four predictableexternal events͑i.e.,worker fatigue,bad weather,delayed mate-rial delivery,and labor disturbance͒into the algorithm they use intheir simulation system.Alvanchi et al.͑2009͒proposed a hybridmodeling system that captures feedback loops derived from an operation model and that can identify the softer aspects of man-agement errors and changes which generate unanticipated distur-bances that affect productivity.Blacud et al.͑2009͒applied concepts of concurrent engineering to deal with disturbances caused by the overlapping of design and construction activities. COPS allows to model operation disturbances by using probabi-listic branching which is formulated by probabilistic arches and normal elements͑refer to Halpin and Riggs1992,pp.272,316͒, but could benefit from any of the methods used by the researchers mentioned above.Case StudiesCase IModeling a Construction Operation NetworkAn operation model of the earthmoving operation described by Halpin and Riggs͑1992͒was reproduced as shown in Fig.7.The operation model is composed of four tasks,namely“load truck,”“travel to dump,”“spot and dump,”and“return to load”as shown in Table1.It is reused to demonstrate the procedure described in Table1.Tasks in Example Operation ModelTask name Task type Required resourcesLoad truck COMBI Loader,empty truck Travel to dump NORMAL Loaded truck Spot and dump COMBI Loaded truck,spotter Return to load NORMAL Emptytruck Fig.7.Operation model established by the DES-based operation analysis module。