Overview of construction simulation approaches to model construction processes

Abstract Construction simulation is a versatile technique with numerous applications. The basic simulation methods are discrete-event simulation (DES), agent-based modeling (ABM), and system dynamics (SD). Depending on the complexity of the problem, using a basic simulation method might not be enough to model construction works appropriately; hybrid approaches are needed. These are combinations of basic methods, or pairings with other techniques, such as fuzzy logic (FL) and neural networks (NNs). This paper presents a framework for applying simulation for problems within the field of construction. It describes DES, SD, and ABM, in addition to presenting how hybrid approaches are most useful in being able to reflect the dynamic nature of construction processes and capture complicated behavior, uncertainties, and dependencies. The examples show the application of the framework for masonry works and how it could be used for obtaining better productivity estimates. Several structures of hybrid simulation are presented alongside their inputs, outputs, and interaction points, which provide a practical reference for researchers on how to implement simulation to model construction systems of labor-intensive activities and lays the groundwork for applications in other construction-related activities.

[1]  D. J. Morrice,et al.  AGENT-BASED MODELING AND SIMULATION IN CONSTRUCTION , 2003 .

[2]  Martin H. Kunc,et al.  System dynamics: A soft and hard approach to modelling , 2017, 2017 Winter Simulation Conference (WSC).

[3]  L. Zadeh,et al.  Fuzzy sets versus probability , 1980, Proceedings of the IEEE.

[4]  Amlan Mukherjee,et al.  Using Agent-Based Modeling to Study Construction Labor Productivity as an Emergent Property of Individual and Crew Interactions , 2009 .

[5]  M. Khanzadi,et al.  Predicting labor costs in construction projects using agent-based modeling and simulation , 2016 .

[6]  Farnad Nasirzadeh,et al.  Integrating system dynamics and fuzzy logic modeling to determine concession period in BOT projects , 2012 .

[7]  Tarek Zayed,et al.  Dynamic Planning of Construction Activities Using Hybrid Simulation , 2013 .

[8]  Osama Moselhi,et al.  Methodology for synchronizing Discrete Event Simulation and System Dynamics models , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[9]  Andreas Tolk,et al.  Hybrid simulation studies and Hybrid Simulation systems: Definitions, challenges, and benefits , 2015, 2015 Winter Simulation Conference (WSC).

[10]  Simaan M. AbouRizk,et al.  Research in Modeling and Simulation for Improving Construction Engineering Operations , 2011 .

[11]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[12]  Laura Florez,et al.  Crew Allocation System for the Masonry Industry , 2017, Comput. Aided Civ. Infrastructure Eng..

[13]  Aminah Robinson Fayek,et al.  Methodology for integrating fuzzy expert systems and discrete event simulation in construction engineering , 2009 .

[14]  Aminah Robinson Fayek,et al.  Predicting Industrial Construction Labor Productivity using Fuzzy Expert Systems , 2005 .

[15]  J. Forrester Industrial Dynamics , 1997 .

[16]  A. Borshchev,et al.  From System Dynamics and Discrete Event to Practical Agent Based Modeling : Reasons , Techniques , Tools , 2004 .

[17]  Simaan M. AbouRizk,et al.  Subjective and interactive duration estimation , 1993 .

[18]  Farnad Nasirzadeh,et al.  A hybrid simulation approach to model and improve construction labor productivity , 2017 .

[19]  Peer-Olaf Siebers,et al.  Discrete-event simulation is dead, long live agent-based simulation! , 2010, J. Simulation.

[20]  Saad H.S. Al-Jibouri,et al.  Modelling construction project productivity using systems dynamics approach , 2009 .

[21]  Lino Guimarães Marujo,et al.  Using fuzzy logic to implement decision policies in system dynamics models , 2016, Expert Syst. Appl..

[22]  Per Hilletofth,et al.  Hybrid simulation models - When, Why, How? , 2010, Expert Syst. Appl..

[23]  Simaan M. AbouRizk,et al.  Role of Simulation in Construction Engineering and Management , 2010 .

[24]  Robert G. Sargent,et al.  An introductory tutorial on verification and validation of simulation models , 2015, 2015 Winter Simulation Conference (WSC).

[25]  L. Florez,et al.  Labor Management in Masonry Construction: A Sustainable Approach , 2014 .

[26]  Eddy M. Rojas,et al.  APPLICATION OF AGENT-BASED MODELING AND SIMULATION TO UNDERSTANDING COMPLEX MANAGEMENT PROBLEMS IN CEM RESEARCH , 2015 .

[27]  Farnad Nasirzadeh,et al.  Prediction and improvement of labor productivity using hybrid system dynamics and agent-based modeling approach , 2018 .

[28]  Miklós Hajdu,et al.  Sensitivity analysis in PERT networks: Does activity duration distribution matter? , 2016 .

[29]  Ivica Završki,et al.  Construction baseline productivity: Theory and practice , 1999 .

[30]  Farnad Nasirzadeh,et al.  A hybrid SD–DES simulation approach to model construction projects , 2015 .

[31]  Andrei Borshchev,et al.  Multi-method modeling , 2013, WSC '13.

[32]  David Arthur Fahrland,et al.  Combined discrete event continuous systems simulation , 1970 .

[33]  Aminah Robinson Fayek,et al.  Overview of fuzzy simulation techniques in construction engineering and management , 2016, 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS).

[34]  Julio C. Martinez Methodology for Conducting Discrete-Event Simulation Studies in Construction Engineering and Management , 2010 .

[35]  Farnad Nasirzadeh,et al.  A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: an application to model construction workers' safety behavior , 2018 .

[36]  Osama Moselhi,et al.  Project-network analysis using fuzzy sets theory , 1996 .

[37]  William Rand,et al.  Understanding the complexity of project team member selection through agent-based modeling , 2016 .

[38]  Geoffrey Gordon,et al.  A general purpose systems simulation program , 1899, AFIPS '61 (Eastern).

[39]  Hong Zhang,et al.  Modeling uncertain activity duration by fuzzy number and discrete-event simulation , 2005, Eur. J. Oper. Res..

[40]  Aminah Robinson Fayek,et al.  Integrating Fuzzy Logic and agent-based modeling for assessing construction crew behavior , 2015, 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC).

[41]  Simaan M. AbouRizk,et al.  Modeling Framework and Architecture of Hybrid System Dynamics and Discrete Event Simulation for Construction , 2011, Comput. Aided Civ. Infrastructure Eng..

[42]  S. Abourizk,et al.  STATISTICAL PROPERTIES OF CONSTRUCTION DURATION DATA , 1992 .

[43]  Ken R. McNaught,et al.  Design classes for hybrid simulations involving agent-based and system dynamics models , 2012, Simul. Model. Pract. Theory.

[44]  Pieter J. Mosterman,et al.  An Overview of Hybrid Simulation Phenomena and Their Support by Simulation Packages , 1999, HSCC.

[45]  Achintya Haldar,et al.  Project Scheduling Using Fuzzy Set Concepts , 1984 .

[46]  Patrick T. Hester,et al.  Towards a theory of multi-method M&S approach: Part I , 2014, Proceedings of the Winter Simulation Conference 2014.

[47]  Alireza Sadeghian,et al.  Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS: A message from the conference organizers , 2010 .

[48]  Feniosky Peña-Mora,et al.  Strategic-Operational Construction Management: Hybrid System Dynamics and Discrete Event Approach , 2008 .