Meta-heuristic approaches for solving Resource Constrained Project Scheduling Problem: A Comparative study

Meta-heuristics for solving Combinatorial Optimization Problems (COP) is a rapidly growing field of research. In this paper we have considered the Resource Constrained Project Scheduling Problem as a COP. The problem is highly constrained and is a common problem for many construction projects. The problem is NP-hard and deterministic methods are slow in execution. In our work, we use Simulated Annealing, Tabu Search, Genetic Algorithm, Particle Swarm Optimization and Elite Particle Swarm Optimization with Mutation for solving benchmark instances of this problem and compare their performances with each other. The results show that Simulated Annealing outperforms other methods in getting optimal results with minimum number of fluctuations.

[1]  Sou-Sen Leu,et al.  GA-BASED MULTICRITERIA OPTIMAL MODEL FOR CONSTRUCTION SCHEDULING , 1999 .

[2]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[3]  Chen Dai,et al.  A comparative study of metaheuristic algorithms for the fertilizer optimization problem , 2006 .

[4]  Wu Cheng.  Hybrid algorithm for complex project scheduling , 2006 .

[5]  Zhang Li-ping,et al.  Optimal choice of parameters for particle swarm optimization , 2005 .

[6]  Yeong-Dae Kim,et al.  Search Heuristics for Resource Constrained Project Scheduling , 1996 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

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

[9]  Erik Demeulemeester,et al.  A branch-and-bound procedure for the multiple resource-constrained project scheduling problem , 1992 .

[10]  Y Cengiz Toklu.  Application of genetic algorithms to construction scheduling with or without resource constraints , 2002 .

[11]  Mao Ning.  An Extension to the DH Branch and Bound Algorithm for MRCPSP , 2001 .

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

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

[14]  Ming Li,et al.  A Particle Swarm Optimization Algorithm with Crossover for Resource Constrained Project Scheduling Problem , 2009, 2009 IITA International Conference on Services Science, Management and Engineering.

[15]  Liu Dong,et al.  Elite Particle Swarm Optimization with mutation , 2008, 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing.

[16]  Hong Zhang,et al.  Particle swarm optimization for resource-constrained project scheduling , 2006 .

[17]  Erwin Pesch,et al.  Evolution based learning in a job shop scheduling environment , 1995, Comput. Oper. Res..

[18]  Yeong-Dae Kim,et al.  A simulated annealing algorithm for resource constrained project scheduling problems , 1997 .

[19]  张丽平,et al.  Optimal choice of parameters for particle swarm optimization , 2005 .

[20]  Comparison of metaheuristic algorithms for Examination Timetabling Problem , 2004 .

[21]  Jan Karel Lenstra,et al.  Scheduling subject to resource constraints: classification and complexity , 1983, Discret. Appl. Math..

[22]  Zahra Naji Azimi.  Comparison of metaheuristics for Examination Timetabling problem , 2004 .

[23]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[24]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[25]  Sönke Hartmann,et al.  A competitive genetic algorithm for resource-constrained project scheduling , 1998 .

[26]  Weng Tat Chan,et al.  CONSTRUCTION RESOURCE SCHEDULING WITH GENETIC ALGORITHMS , 1996 .

[27]  F. F. Boctor,et al.  Some efficient multi-heuristic procedures for resource-constrained project scheduling , 1990 .

[28]  Sönke Hartmann,et al.  A self‐adapting genetic algorithm for project scheduling under resource constraints , 2002 .

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