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Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

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  • Pickardt, Christoph W.
  • Hildebrandt, Torsten
  • Branke, Jürgen
  • Heger, Jens
  • Scholz-Reiter, Bernd

Abstract

We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:67-77
    DOI: 10.1016/j.ijpe.2012.10.016
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    Cited by:

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    3. Shijin Wang & Ming Liu, 2016. "Two-machine flow shop scheduling integrated with preventive maintenance planning," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(3), pages 672-690, February.
    4. Yikai Ma & Wenjuan Zhang & Juergen Branke, 2024. "Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms," Journal of Heuristics, Springer, vol. 30(5), pages 423-451, December.
    5. 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).
    6. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.
    7. Sungwook Yoon & Jihyun Kim & Sukjae Jeong, 2017. "RETRACTED: The Optimal Decision Combination in Semiconductor Manufacturing," Sustainability, MDPI, vol. 9(10), pages 1, October.
    8. Jing, Hao & Sheng, Lijuan & Luo, Chaorui & Kwak, Choonjong, 2021. "Statistical analysis of family based dispatching rules and preemption," International Journal of Production Economics, Elsevier, vol. 240(C).
    9. Helga Ingimundardottir & Thomas Philip Runarsson, 2018. "Discovering dispatching rules from data using imitation learning: A case study for the job-shop problem," Journal of Scheduling, Springer, vol. 21(4), pages 413-428, August.
    10. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Hurtado, Margarita, 2018. "Exploiting the characteristics of serial queues to reduce the mean and variance of flow time using combined priority rules," International Journal of Production Economics, Elsevier, vol. 196(C), pages 211-225.

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