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Clustering and Dispatching Rule Selection Framework for Batch Scheduling

Author

Listed:
  • Gilseung Ahn

    (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea)

  • Sun Hur

    (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea)

Abstract

In this study, a batch scheduling with job grouping and batch sequencing is considered. A clustering algorithm and dispatching rule selection model is developed to minimize total tardiness. The model and algorithm are based on the constrained k-means algorithm and neural network. We also develop a method to generate a training dataset from historical data to train the neural network. We use numerical examples to demonstrate that the proposed algorithm and model efficiently and effectively solve batch scheduling problems.

Suggested Citation

  • Gilseung Ahn & Sun Hur, 2020. "Clustering and Dispatching Rule Selection Framework for Batch Scheduling," Mathematics, MDPI, vol. 8(1), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:1:p:80-:d:304839
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    References listed on IDEAS

    as
    1. Xu, Rui & Chen, Huaping & Li, Xueping, 2013. "A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system," International Journal of Production Economics, Elsevier, vol. 145(1), pages 371-386.
    2. Gilseung Ahn & Myunghwan Park & You-Jin Park & Sun Hur, 2019. "Interactive Q-Learning Approach for Pick-and-Place Optimization of the Die Attach Process in the Semiconductor Industry," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, February.
    Full references (including those not matched with items on IDEAS)

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