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A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems

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  • Braune, Roland
  • Benda, Frank
  • Doerner, Karl F.
  • Hartl, Richard F.

Abstract

This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP.

Suggested Citation

  • Braune, Roland & Benda, Frank & Doerner, Karl F. & Hartl, Richard F., 2022. "A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems," International Journal of Production Economics, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:proeco:v:243:y:2022:i:c:s0925527321003182
    DOI: 10.1016/j.ijpe.2021.108342
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    References listed on IDEAS

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    1. Yong Zhou & Jian-jun Yang & Zhuang Huang, 2020. "Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2561-2580, May.
    2. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    3. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    4. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
    5. Frank Benda & Roland Braune & Karl F. Doerner & Richard F. Hartl, 2019. "A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 871-893, December.
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