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An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem

Author

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  • Hadi Mokhtari

    (University of Kashan)

  • Amir Noroozi

    (Iran University of Science and Technology)

Abstract

The flow shop is a well-known class of manufacturing system for production process planning. The need for scheduling approaches arises from the requirement of most systems to implement more than one process at a moment. Batch processing is usually carried out to load balance and share system resources effectively and gain a desired quality of service level. A flow shop manufacturing problem with batch processors (BP) is discussed in current paper so as to minimize total penalty of earliness and tardiness. To address the problem, two improved discrete particle swarm optimization (PSO) algorithms are designed where most important properties of basic PSO on velocity of particles are enhanced. We also employ the attractive properties of logistic chaotic map within PSO so as to investigate the influence of chaos on search performance of BP flow shop problem. In order to investigate the suggested algorithms, a comprehensive computational study is carried out and performance of algorithms is compared with (1) a commercial optimization solver, (2) a well-known algorithm from PSO’s literature and (3) three algorithms from BP’s literature. The experimental results demonstrate the superiority of our algorithm against others.

Suggested Citation

  • Hadi Mokhtari & Amir Noroozi, 2018. "An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1063-1081, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1158-x
    DOI: 10.1007/s10845-015-1158-x
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    References listed on IDEAS

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    2. Uğur Erkin Kocamaz & Alper Göksu & Harun Taşkın & Yılmaz Uyaroğlu, 2021. "Control of chaotic two-predator one-prey model with single state control signals," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1563-1572, August.

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