Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

James C. Chen; Wun Hao Jaong; Cheng Ju Sun; Hung Yu Lee; Jenn Sheng Wu; Chung Chao Ku

doi:10.4028/www.scientific.net/KEM.419-420.633

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 419-420Applying Genetic Algorithm to Resource Constrained...

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

Abstract:

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

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Periodical:

Key Engineering Materials (Volumes 419-420)

Pages:

633-636

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.419-420.633

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Cite this paper

Online since:

October 2009

Authors:

James C. Chen, Wun Hao Jaong, Cheng Ju Sun, Hung Yu Lee, Jenn Sheng Wu, Chung Chao Ku

Keywords:

Genetic Algorithm (GA), Heuristic Method, Resource-Constrained Multi-Project Scheduling Problems

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References

[1] J. F. Goncalves, J. J. M. Mendes, and M. G. C. Resende, A Genetic Algorithm for the Resource Constrained Multi-Project Scheduling Problem, European Journal of Operational Research, Vol. 189 (2008), pp.1171-1190.

DOI: 10.1016/j.ejor.2006.06.074

Google Scholar

[2] J. J. Grefenstette, Optimization of Control Parameters for Genetic Algorithms, IEEE Transactions on Systems, Man and Cybernetics, Vol. 16 (1986), pp.122-128.

DOI: 10.1109/tsmc.1986.289288

Google Scholar

[3] S. Hartmann, A Competitive Genetic Algorithm for Resource-Constrained Project Scheduling, Naval Research Logistics, Vol. 45 (1998), pp.733-750.

DOI: 10.1002/(sici)1520-6750(199810)45:7<733::aid-nav5>3.0.co;2-c

Google Scholar

[4] K. W. Kim, Y. S. Yun, J. M. Yoon, M. Gen and G. Yamazaki, Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling, Computers in Industry, Vol. 56 (2005), pp.143-160.

DOI: 10.1016/j.compind.2004.06.006

Google Scholar

[5] J. D. Schaffer, A. Richard, L. Caruana, J. Eshelman, and D. Rajarshi, A Study of Control Parameters Affecting Online Performance of Genetic Algorithm for Function Optimization, The 3rd International Conference on Genetic Algorithms and Their Applications (1989).

Google Scholar