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A hybrid artificial bee colony algorithm for the job shop scheduling problem

Author

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  • Zhang, Rui
  • Song, Shiji
  • Wu, Cheng

Abstract

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.

Suggested Citation

  • Zhang, Rui & Song, Shiji & Wu, Cheng, 2013. "A hybrid artificial bee colony algorithm for the job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 167-178.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:167-178
    DOI: 10.1016/j.ijpe.2012.03.035
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    References listed on IDEAS

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    1. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
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    Cited by:

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    2. Liang Tang & Zhihong Jin & Xuwei Qin & Ke Jing, 2019. "Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design," Annals of Operations Research, Springer, vol. 275(2), pages 685-714, April.
    3. De Vincenzo, Ilario & Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe & Grigolini, Paolo, 2018. "Mimicking the collective intelligence of human groups as an optimization tool for complex problems," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 259-266.
    4. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.
    5. Ng, K.K.H. & Lee, C.K.M. & Chan, Felix T.S. & Qin, Yichen, 2017. "Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 115-136.

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