Integrating Messy Genetic Algorithms and Simulation to Optimize Resource Utilization

: This article presents a mechanism for integrating messy genetic algorithms (MGAs) and a discrete event simulation technique to facilitate the simulation of optimal resource utilization to enhance system performance, such as in relation to the production rate or unit cost. Various resource distribution modeling scenarios were tested in simulation to determine their system performances. MGA operations were then applied in the selection of the best resource utilization schemes based on those performances. A case study showed that this new modeling mechanism, along with the implemented computer program, could not only ease the process of developing optimal resource utilization, but could also improve the system performance of the simulation model.

[1]  Hojjat Adeli,et al.  Bilevel Parallel Genetic Algorithms for Optimization of Large Steel Structures , 2001 .

[2]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[3]  Hojjat Adeli,et al.  Resource Scheduling Using Neural Dynamics Model of Adeli and Park , 2001 .

[4]  Chung-Wei Feng,et al.  An effective simulation mechanism for construction operations , 2003 .

[5]  Hojjat Adeli,et al.  Optimization of space structures by neural dynamics , 1995, Neural Networks.

[6]  Leland Stanford Riggs Sensitivity analysis of construction operations , 1979 .

[7]  Dragan Savic,et al.  WATER NETWORK REHABILITATION WITH STRUCTURED MESSY GENETIC ALGORITHM , 1997 .

[8]  Hojjat Adeli,et al.  Concurrent Structural Optimization on Massively Parallel Supercomputer , 1995 .

[9]  Asim Karim,et al.  OO Information Model for Construction Project Management , 1999 .

[10]  Angus R. Simpson,et al.  Competent Genetic-Evolutionary Optimization of Water Distribution Systems , 2001 .

[11]  Kamal C. Sarma,et al.  FUZZY GENETIC ALGORITHM FOR OPTIMIZATION OF STEEL STRUCTURES , 2000 .

[12]  Chung-Wei Feng,et al.  A hybrid mechanism for optimizing construction simulation models , 2005 .

[13]  Asim Karim,et al.  CONSCOM: An OO Construction Scheduling and Change Management System , 1999 .

[14]  Hojjat Adeli,et al.  Life‐cycle cost optimization of steel structures , 2002 .

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

[16]  Daniel W. Halpin,et al.  SIMULATION OF CONCRETE BATCH PLANT PRODUCTION , 2001 .

[17]  H. Adeli,et al.  Augmented Lagrangian genetic algorithm for structural optimization , 1994 .

[18]  Jingsheng Shi,et al.  Automated Construction‐Simulation Optimization , 1994 .

[19]  Hojjat Adeli,et al.  Scheduling/Cost Optimization and Neural Dynamics Model for Construction , 1997 .

[20]  Tarek Hegazy,et al.  Resource Optimization Using Combined Simulation and Genetic Algorithms , 2003 .

[21]  Hojjat Adeli,et al.  Distributed Genetic Algorithm for Structural Optimization , 1995 .

[22]  Anil Sawhney,et al.  A Discrete Event Simulation Model to Analyze the Residential Construction Inspection Process , 2005 .

[23]  Kamal C. Sarma Fuzzy discrete multicriteria cost optimization of steel structures using genetic algorithm , 2000 .

[24]  Hojjat Adeli,et al.  Distributed neural dynamics algorithms for optimization of large steel structures , 1997 .

[25]  H. Adeli,et al.  Integrated Genetic Algorithm for Optimization of Space Structures , 1993 .

[26]  Mohamed Marzouk,et al.  Multiobjective Optimization of Earthmoving Operations , 2004 .

[27]  H. Adeli,et al.  Concurrent genetic algorithms for optimization of large structures , 1994 .