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Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Author

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  • Mohamed Habib Zahmani

    (University of Mostaganem
    University of Oran 1 Ahmed Benbella)

  • Baghdad Atmani

    (University of Oran 1 Ahmed Benbella)

Abstract

In production planning and scheduling, data mining methods can be applied to transform the scheduling data into useful knowledge that can be used to improve planning/scheduling by enabling real-time decision-making. In this paper, a novel approach combining dispatching rules, a genetic algorithm, data mining, and simulation is proposed. The genetic algorithm (i) is used to solve scheduling problems, and the obtained solutions (ii) are analyzed in order to extract knowledge, which is then used (iii) to automatically assign in real-time different dispatching rules to machines based on the jobs in their respective queues. The experiments are conducted on a job shop scheduling problem with a makespan criterion. The obtained results from the computational study show that the proposed approach is a viable and effective approach for solving the job shop scheduling problem in real time.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jsched:v:24:y:2021:i:2:d:10.1007_s10951-020-00664-5
    DOI: 10.1007/s10951-020-00664-5
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    References listed on IDEAS

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    1. T. C. E. Cheng & Bo Peng & Zhipeng Lü, 2016. "A hybrid evolutionary algorithm to solve the job shop scheduling problem," Annals of Operations Research, Springer, vol. 242(2), pages 223-237, July.
    2. Chris N. Potts & Luk N. Van Wassenhove, 1985. "A Branch and Bound Algorithm for the Total Weighted Tardiness Problem," Operations Research, INFORMS, vol. 33(2), pages 363-377, April.
    3. Demirkol, Ebru & Mehta, Sanjay & Uzsoy, Reha, 1998. "Benchmarks for shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 109(1), pages 137-141, August.
    4. 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.
    5. 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.
    6. 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.
    7. Raghu, T. S. & Rajendran, Chandrasekharan, 1993. "An efficient dynamic dispatching rule for scheduling in a job shop," International Journal of Production Economics, Elsevier, vol. 32(3), pages 301-313, November.
    8. Pu-Hai Chiang & Chau-Chen Torng, 2016. "A production planning and optimisation of multi-mode job shop scheduling problem for an avionics manufacturing plant," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 30(3/4), pages 179-195.
    9. S. Karthikeyan & P. Asokan & S. Nickolas & Tom Page, 2012. "Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 26(1/2/3/4), pages 81-103.
    Full references (including those not matched with items on IDEAS)

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