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A Hybrid Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem with Setup Times

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Advances in Swarm Intelligence (ICSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12690))

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Abstract

This paper studies the multi-objective permutation flow shop scheduling problem (PFSP) with setup times. Firstly, the mathematical model of multi-objective PFSP with setup time is established, then based on the theory of Pareto, Genetic algorithm and Variable Neighborhood Search, a new hybrid algorithm is proposed, named as Multiple Objective Hybrid Genetic algorithm (MOHGA). Finally, a set of benchmark instances with different scales are used to evaluate the performance of MOHGA. Experimental results show that the MOHGA obtains some solutions better than those previously reported in the literature, which reveals that the proposed MOHGA is an effective approach for the optimization of multi-objective PFSP with setup time.

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Acknowledgment

This research work is supported by the National Key R&D Program of China under Grant No. 2018AAA0101700, and the Program for HUST Academic Frontier Youth Team under Grant No. 2017QYTD04.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Cuiyu Wang, Shuting Wang & Xinyu Li

Authors
  1. Cuiyu Wang
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  2. Shuting Wang
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  3. Xinyu Li
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Corresponding author

Correspondence to Xinyu Li .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Wang, C., Wang, S., Li, X. (2021). A Hybrid Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem with Setup Times. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-78811-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78810-0

  • Online ISBN: 978-3-030-78811-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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