HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM

The optimal solutions for the resource allocation problem are of great significant to project planners for distributing their available resources into the activities most effectively. Many studies have been undertaken to solve the resource-constrained project scheduling problems using genetic algorithms, which have been proven as an effective and efficient optimization tool to solve difficult and complex problems. One of the trends in the genetic algorithm research study is to develop a hybrid meta-heuristic method using artificial intelligence and biologically-inspired techniques. In an effort to address this issue, the author developed a new hybrid genetic algorithm to solve the construction resource-constrained project scheduling problems. This paper evaluates the parameter effects of the hybrid genetic algorithm for optimization because optimal settings of the genetic algorithm parameters such as population size, crossover probability, and mutation probability, are critical conditions in producing the best value for the outcomes.

[1]  Erik Demeulemeester,et al.  New Benchmark Results for the Resource-Constrained Project Scheduling Problem , 1997 .

[2]  George S. Dulikravich,et al.  Three-Dimensional Aerodynamic Shape Optimization Using Genetic and Gradient Search Algorithms , 1997 .

[3]  Armin Scholl,et al.  Computing lower bounds by destructive improvement: An application to resource-constrained project scheduling , 1999, Eur. J. Oper. Res..

[4]  David E. Goldberg,et al.  Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design , 2005 .

[5]  Peter Brucker,et al.  A branch and bound algorithm for the resource-constrained project scheduling problem , 1998, Eur. J. Oper. Res..

[6]  Ralph D. Ellis,et al.  Robust global and local search approach to resource-constrained project scheduling , 2009 .

[7]  V. Maniezzo,et al.  An Exact Algorithm for the Resource-Constrained Project Scheduling Problem Based on a New Mathematical Formulation , 1998 .

[8]  David E. Goldberg,et al.  Decision making in a hybrid genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[9]  Arno Sprecher,et al.  Multi-mode resource-constrained project scheduling by a simple, general and powerful sequencing algorithm , 1998, Eur. J. Oper. Res..

[10]  David E. Goldberg,et al.  Optimizing Global-Local Search Hybrids , 1999, GECCO.

[11]  Rainer Kolisch,et al.  Experimental investigation of heuristics for resource-constrained project scheduling: An update , 2006, Eur. J. Oper. Res..

[12]  Liang-Cheng Chang,et al.  Dynamic Optimal Groundwater Management with Inclusion of Fixed Costs , 2002 .

[13]  Rolf H. Möhring,et al.  Resource-constrained project scheduling: Notation, classification, models, and methods , 1999, Eur. J. Oper. Res..

[14]  Angelo M. Sabatini,et al.  A hybrid genetic algorithm for estimating the optimal time scale of linear systems approximations using Laguerre models , 2000, IEEE Trans. Autom. Control..