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Learning effective new single machine dispatching rules from optimal scheduling data

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  • Olafsson, Sigurdur
  • Li, Xiaonan

Abstract

The expertise of the scheduler plays an important role in creating production schedules, and the schedules created in the past thus provide important information about how they should be done in the future. Motivated by this observation, we learn new scheduling rules from existing schedules using data mining techniques. However, direct data mining of scheduling data can at best mimic existing scheduling practices. We therefore propose a novel two-phase approach for learning, where we first learn which part of the data correspond to best scheduling practices and then use this data and decision tree induction to learn new and previously unknown dispatching rules. Our numerical results indicate that the newly learned rules can be a significant improvement upon the underlying scheduling rules, thus going beyond mimicking existing practice.

Suggested Citation

  • Olafsson, Sigurdur & Li, Xiaonan, 2010. "Learning effective new single machine dispatching rules from optimal scheduling data," International Journal of Production Economics, Elsevier, vol. 128(1), pages 118-126, November.
  • Handle: RePEc:eee:proeco:v:128:y:2010:i:1:p:118-126
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    References listed on IDEAS

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    Cited by:

    1. A. S. Xanthopoulos & D. E. Koulouriotis, 2018. "Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 69-91, January.
    2. Anran Zhao & Peng Liu & Xiyu Gao & Guotai Huang & Xiuguang Yang & Yuan Ma & Zheyu Xie & Yunfeng Li, 2022. "Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(23), pages 1-30, December.
    3. Mohamed Habib Zahmani & Baghdad Atmani, 2021. "Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation," Journal of Scheduling, Springer, vol. 24(2), pages 175-196, April.
    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. 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.
    6. 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.
    7. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.
    8. Fan Xue & C. Chan & W. Ip & C. Cheung, 2011. "A learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems," Netnomics, Springer, vol. 12(3), pages 183-207, October.

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