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A multi-objective particle swarm for a flow shop scheduling problem

Author

Listed:
  • A. R. Rahimi-Vahed

    (University of Tehran)

  • S. M. Mirghorbani

    (University of Tehran)

Abstract

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

Suggested Citation

  • A. R. Rahimi-Vahed & S. M. Mirghorbani, 2007. "A multi-objective particle swarm for a flow shop scheduling problem," Journal of Combinatorial Optimization, Springer, vol. 13(1), pages 79-102, January.
    • Handle: RePEc:spr:jcomop:v:13:y:2007:i:1:d:10.1007_s10878-006-9015-7
    DOI: 10.1007/s10878-006-9015-7
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    References listed on IDEAS

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    5. Toktas, Berkin & Azizoglu, Meral & Koksalan, Suna Kondakci, 2004. "Two-machine flow shop scheduling with two criteria: Maximum earliness and makespan," European Journal of Operational Research, Elsevier, vol. 157(2), pages 286-295, September.
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    8. Sayin, Serpil & Karabati, Selcuk, 1999. "A bicriteria approach to the two-machine flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 113(2), pages 435-449, March.
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    Cited by:

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    3. Jianhui Mou & Xinyu Li & Liang Gao & Wenchao Yi, 2018. "An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 789-807, April.

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