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A Multi-objective Particle Swarm Optimization Algorithm Embedded with Maximum Fitness Function for Dual-Resources Constrained Flexible Job Shop Scheduling

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12836))

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Abstract

This paper is concerned with a multiple-objective flexible job-shop scheduling problem with dual-resources constraints. Both time and cost-concerned objectives are taken into consideration and the corresponding mathematical model is presented. Based on Maximal fitness function, a hybrid discrete particle swarm algorithm is proposed to effectively solve the problem. The global and local search ability of the algorithm are both improved by modifying the position updating method and simulating annealing strategy with Maximal fitness function. Moreover, external archive is used to reserve better particles. Finally, the effectiveness of the proposed algorithm demonstrated by simulation examples and the results show that the obtained solutions are more uniformly distributed towards the Pareto solutions.

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Acknowledgment

This research work was partly supported by Natural Science Foundation of Zhejiang Province (Grant No. LGF21G030001) and General Projects of Zhejiang Educational Committee (Grant No. Y201839027).

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Authors and Affiliations

  1. Department of Computer and Information Security, Zhejiang Police College, Hangzhou, Zhejiang, China

    Jing Zhang

  2. College of Automation and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China

    Jing Jie

Authors
  1. Jing Zhang
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  2. Jing Jie
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Corresponding author

Correspondence to Jing Jie .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

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Zhang, J., Jie, J. (2021). A Multi-objective Particle Swarm Optimization Algorithm Embedded with Maximum Fitness Function for Dual-Resources Constrained Flexible Job Shop Scheduling. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_59

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  • DOI: https://doi.org/10.1007/978-3-030-84522-3_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84521-6

  • Online ISBN: 978-3-030-84522-3

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