Finding Optimum Resource Allocation to Optimizing Construction Project Time/Cost through Combination of Artificial Agents CPM and GA

In order to plan a construction project, computer simulations are frequently used to predict the performance of the operations through simulating the process flows and resource selection procedure. However, for finding the optimum resource allocation of the construction activities, all possible combinations must be tested through simulation study. If the number of activities and allocated resources are high, the numbers of these combinations become too large, then this process would not be economical task to do. Therefore, simulation analysis is no longer considered through an optimization technique. Using of Genetic Algorithms (GA) is one of the simple and widely used tools for optimizing heavy intensive engineering problems which can covers various areas of research. With keeping this in mind, this study presented a new hybrid model which integrated agent based modeling with CPM and GA to find out the best resource allocation combination for the construction project’s activities. Based on the results obtained, this new hybrid model can eectively find the optimum resource allocation with respect to time, cost, or any combination of time-cost.

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

[2]  Tao-ming Cheng,et al.  Integrating Messy Genetic Algorithms and Simulation to Optimize Resource Utilization , 2009, Comput. Aided Civ. Infrastructure Eng..

[3]  Tarek Hegazy,et al.  MOST‐FIT: Support Techniques for Inspection and Life Cycle Optimization in Building Asset Management , 2012, Comput. Aided Civ. Infrastructure Eng..

[4]  Nashwan Dawood,et al.  Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms , 2010, Comput. Aided Civ. Infrastructure Eng..

[5]  James Moffat,et al.  Emergent behaviour: Theory and experimentation using the MANA model , 2006, Adv. Decis. Sci..

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

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

[8]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[9]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[10]  Lai Ming,et al.  Nonlinear structural response prediction based on support vector machines , 2008 .

[11]  Franco Bontempi,et al.  Genetic Algorithms for the Dependability Assurance in the Design of a Long‐Span Suspension Bridge , 2012, Comput. Aided Civ. Infrastructure Eng..

[12]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[13]  A. Lipsett.  NONLINEAR STRUCTURAL RESPONSE IN RANDOM WAVES , 1986 .

[14]  Shih-Hsu Wang,et al.  Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction , 2012, Comput. Aided Civ. Infrastructure Eng..

[15]  G. An,et al.  The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity , 2007, Theoretical Biology and Medical Modelling.

[16]  Charles M. Macal,et al.  Tutorial on agent-based modeling and simulation , 2005 .

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

[18]  Simaan M. AbouRizk,et al.  Using Simulation to Plan Tunnel Construction , 2013 .

[19]  J Baker,et al.  REDUCING BIAS AND NEFFICIENCY IN THE SELECTION ALGORITHM, GENETIC ALGORITHMS AND APPLICATIONS , 2000 .

[20]  Alexander J. Smola,et al.  Regression estimation with support vector learning machines , 1996 .

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

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

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[25]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[26]  David Arditi,et al.  Resource Leveling in Line‐of‐Balance Scheduling , 2013, Comput. Aided Civ. Infrastructure Eng..

[27]  Kin Keung Lai,et al.  An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring , 2009, Eur. J. Oper. Res..

[28]  Charles M. Macal,et al.  Multiscale agent-based consumer market modeling , 2010 .

[29]  Mahesh Pal,et al.  Support vector regression based shear strength modelling of deep beams , 2011 .

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

[31]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

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