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A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming

Published:08 July 2020Publication History

ABSTRACT

Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for job shop scheduling. However, the environments of job shops vary in configurations, and the scheduling heuristic for each job shop is normally trained independently, which leads to low efficiency for solving multiple job shop scheduling problems. This paper introduces the idea of multitasking to genetic programming to improve the efficiency of solving multiple dynamic flexible job shop scheduling problems with scheduling heuristics. It is realised by the proposed evolutionary framework and knowledge transfer mechanism for genetic programming to train scheduling heuristics for different tasks simultaneously. The results show that the proposed algorithm can dramatically reduce the training time for solving multiple dynamic flexible job shop tasks.

References

  1. Juergen Branke, Su Nguyen, Christoph W Pickardt, and Mengjie Zhang. 2016. Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation 20, 1 (2016), 110--124.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Abhishek Gupta, Yew-Soon Ong, Liang Feng, and Kay Chen Tan. 2017. Multiobjective Multifactorial Optimization in Evolutionary Multitasking. IEEE Transactions on Cybernetics 47, 7 (2017), 1652--1665.Google ScholarGoogle ScholarCross RefCross Ref
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  4. Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2018. Genetic programming with multi-tree representation for dynamic flexible job shop scheduling. In Proceedings of the Australasian Joint Conference on Artificial Intelligence. Springer, 472--484.Google ScholarGoogle ScholarCross RefCross Ref
  5. Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2019. A Two-Stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling. In Proceedings of the Genetic and Evolutionary Computation Conference. IEEE, 347--355.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming

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    • Published in

      cover image ACM Conferences
      GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
      July 2020
      1982 pages
      ISBN:9781450371278
      DOI:10.1145/3377929

      Copyright © 2020 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 July 2020

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      Overall Acceptance Rate1,669of4,410submissions,38%

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