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A Particle Swarm Optimization Algorithm On Job-Shop Scheduling Problems With Multi-Purpose Machines

Author

Listed:
  • PISUT PONGCHAIRERKS

    (Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12121, Thailand)

  • VORATAS KACHITVICHYANUKUL

    (Industrial Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, 12120, Thailand)

Abstract

This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOcis a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOcin MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOccombined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.

Suggested Citation

  • Pisut Pongchairerks & Voratas Kachitvichyanukul, 2009. "A Particle Swarm Optimization Algorithm On Job-Shop Scheduling Problems With Multi-Purpose Machines," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 26(02), pages 161-184.
    • Handle: RePEc:wsi:apjorx:v:26:y:2009:i:02:n:s0217595909002158
    DOI: 10.1142/S0217595909002158
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    Cited by:

    1. Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, March.

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