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:: Volume 2, Issue 3 (11-2015) ::
2015, 2(3): 905-924 Back to browse issues page
An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation
Mohammad Hassan Sebt * 1, Mohammad Reza Afshar2 , Yagub Alipouri2
1- Amirkabir University of Technology, Tehran, Iran , sebt@aut.ac.ir
2- Amirkabir University of Technology, Tehran, Iran
Abstract:   (4426 Views)
In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.
Keywords: Combinatorial Optimization, Multi-mode project scheduling, Resource constraints, Genetic Algorithm, Random key representation
     
Type of Study: مقاله پژوهشی |
ePublished: 2017/09/28
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Sebt M H, Afshar M R, Alipouri Y. An Efficient Genetic Agorithm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem Based on Random Key Representation. Journal title 2015; 2 (3) :905-924
URL: http://system.khu.ac.ir/ijsom/article-1-2545-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 2, Issue 3 (11-2015) Back to browse issues page
International Journal of Supply and Operations Management International Journal of Supply and Operations Management
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