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A flexible dispatching rule for minimizing tardiness in job shop scheduling

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  • Chen, Binchao
  • Matis, Timothy I.

Abstract

In this paper, a dispatching rule called the Weight Biased Modified RRrule is developed that minimizes the mean tardiness of weighted jobs in an m-machine job shop, i.e. Jm|ri,recrc|∑iTik where Tik denotes the tardiness of those jobs with weight greater than a specified threshold level k. It is a significant extension of the RRrule in that it has linear complexity and considers weighted jobs. In addition, the WBMR rule allows for biasing of the schedule towards meeting the deadline of high priority jobs through the tuning of a single parameter, where such an effect is quantified by evaluating tardiness at different truncation thresholds. Numerical testing demonstrates the ability of the WBMR to outperform other traditional rules at various congestion and due-date tightness levels.

Suggested Citation

  • Chen, Binchao & Matis, Timothy I., 2013. "A flexible dispatching rule for minimizing tardiness in job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 360-365.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:360-365
    DOI: 10.1016/j.ijpe.2012.08.019
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Min & Huang, George Q., 2022. "Intralogistics synchronization in robotic forward-reserve warehouses for e-commerce last-mile delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    3. Hübl, Alexander & Jodlbauer, Herbert & Altendorfer, Klaus, 2013. "Influence of dispatching rules on average production lead time for multi-stage production systems," International Journal of Production Economics, Elsevier, vol. 144(2), pages 479-484.
    4. Karapetyan, Daniel & Mitrovic Minic, Snezana & Malladi, Krishna T. & Punnen, Abraham P., 2015. "Satellite downlink scheduling problem: A case study," Omega, Elsevier, vol. 53(C), pages 115-123.
    5. 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.
    6. Tanja Mlinar & Philippe Chevalier, 2016. "Pooling heterogeneous products for manufacturing environments," 4OR, Springer, vol. 14(2), pages 173-200, June.
    7. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    8. Mallor, Fermin & Guardiola, Ivan G., 2014. "The Weibull scheduling index for client driven manufacturing processes," International Journal of Production Economics, Elsevier, vol. 150(C), pages 225-238.
    9. 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.

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