An effective simulation mechanism for construction operations

Abstract Construction project planners have used computer simulation to predict the performance of the construction operation in terms of process flows and resource selection for decades. Traditionally, however, to find the best resource utilization of the construction operation, all possible resource combinations should be tested within the simulation process. That is, an exhaustive enumeration of resource combinations should be conducted, which is not economic if the possible resource combinations increase explosively. Therefore, simulation is not considered as an optimization technique. Genetic Algorithms (GA), developed by John Holland in 1975, has been widely applied to different disciplines of research for solving the optimization problems. GA is also known for its flexibility in hybridizing with other methodologies to obtain better solutions of the simulation systems. This paper presents a new mechanism that integrates simulation with GA to find the best resource combination for the construction operation. Results show that the new simulation mechanism can effectively locate the optimal resource combination for the construction operation and enhance the optimization capability of the construction simulation. In addition, a user-friendly computer simulation system—Genetic Algorithms with Construction Operation Simulation Tool (GACOST) was developed to provide the construction planner an efficient means of analyzing and optimizing the construction operation.

[1]  Lloyd A. Smith,et al.  Optimisation techniques for a computer simulation of a pastoral dairy farm , 1998 .

[2]  Roger McHaney.  Integration of the Genetic Algorithm and Discrete-Event Computer Simulation for Decision Support , 1999, Simul..

[3]  Farhad Azadivar,et al.  Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach , 1999, Eur. J. Oper. Res..

[4]  Walter D. Potter,et al.  Farm Machinery Selection Using Simulation and Genetic Algorithms , 1996 .

[5]  James David Lutz.  Planning of linear construction projects using *simulation and line of balance , 1990 .

[6]  Daniel W. Halpin,et al.  CYCLONE — Method for Modeling Job Site Processes , 1977 .

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Z. Y. Zhao,et al.  Genetic Production Line-Balancing for the Hard Disk Drive Industry , 2000 .

[9]  Tao-Ming Cheng,et al.  Construction Operation Simulation Tool - Cost , 2000 .

[10]  Chung-Wei Feng,et al.  Stochastic construction time-cost trade-off analysis , 2000 .

[11]  Ray J. Paul,et al.  Simulation optimisation using a genetic algorithm , 1998, Simul. Pract. Theory.

[12]  Daniel W. Halpin,et al.  Planning and analysis of construction operations , 1992 .

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  William J. Stevenson,et al.  Introduction to management science , 1992 .

[15]  Agostino G. Bruzzone,et al.  Simulation and Genetic Algorithms for Ship Planning and Shipyard Layout , 1998, Simul..

[16]  Simaan M. AbouRizk,et al.  Comparison of Two Simulation Methodologies in Construction , 1993 .