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An Improved NSGA-II for Solving Reentrant Flexible Assembly Job Shop Scheduling Problem

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Advances in Swarm Intelligence (ICSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13968))

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Abstract

In the wafer manufacturing process of micro electro mechanical systems (MEMS), there are reentrant flow, parallel machines, and assembly operation. Therefore, this study models its scheduling problem as a reentrant flexible assembly job shop scheduling problem. First, a mathematical model is formulated to minimize the total tardiness and the total energy consumption. Second, an improved non-dominated sorting genetic algorithm II (INSGA-II) is proposed to solve this NP-hard problem. An encoding and decoding method are designed according to the problem characteristics. A rule-based initialization strategy is developed to improve the quality of the initialized population. Specific crossover, mutation and selection operators are designed. Finally, numerical experiments are carried out, and the result shows that the proposed algorithm can effectively solve the problem.

Supported by the National Natural Science Foundation of China (No. 52175449).

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Authors and Affiliations

  1. College of Mechanical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, China

    Xiuli Wu, Yaqi Zhang & Kunhai Zhao

Authors
  1. Xiuli Wu
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  2. Yaqi Zhang
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  3. Kunhai Zhao
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Corresponding author

Correspondence to Xiuli Wu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Harbin Institute of Technology, Shenzhen, China

    Wenjian Luo

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Wu, X., Zhang, Y., Zhao, K. (2023). An Improved NSGA-II for Solving Reentrant Flexible Assembly Job Shop Scheduling Problem. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-36622-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36621-5

  • Online ISBN: 978-3-031-36622-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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