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Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm

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  • S-W Lin

    (Department of Information Management, Chang Gung University, Taoyuan, Taiwan, R.O.C)

  • K-C Ying

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, R.O.C)

Abstract

The order acceptance and scheduling (OAS) problem is an important topic for make-to-order production systems with limited production capacity and tight delivery requirements. This paper proposes a new algorithm based on Artificial Bee Colony (ABC) for solving the single machine OAS problem with release dates and sequence-dependent setup times. The performance of the proposed ABC-based algorithm was validated by a benchmark problem set of test instances with up to 100 orders. Experimental results showed that the proposed ABC-based algorithm outperformed three state-of-art metaheuristic-based algorithms from the literature. It is believed that this study successfully demonstrates a high-performance algorithm that can serve as a new benchmark approach for future research on the OAS problem addressed in this study.

Suggested Citation

  • S-W Lin & K-C Ying, 2013. "Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(2), pages 293-311, February.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:2:p:293-311
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    Citations

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    Cited by:

    1. Esmaeilbeigi, Rasul & Charkhgard, Parisa & Charkhgard, Hadi, 2016. "Order acceptance and scheduling problems in two-machine flow shops: New mixed integer programming formulations," European Journal of Operational Research, Elsevier, vol. 251(2), pages 419-431.
    2. Xin Li & José A. Ventura & Kevin A. Bunn, 2021. "A joint order acceptance and scheduling problem with earliness and tardiness penalties considering overtime," Journal of Scheduling, Springer, vol. 24(1), pages 49-68, February.
    3. Lei He & Mathijs Weerdt & Neil Yorke-Smith, 2020. "Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1051-1078, April.
    4. Li, Xin & Ventura, Jose A., 2020. "Exact algorithms for a joint order acceptance and scheduling problem," International Journal of Production Economics, Elsevier, vol. 223(C).
    5. Somaye Geramipour & Ghasem Moslehi & Mohammad Reisi-Nafchi, 2017. "Maximizing the profit in customer’s order acceptance and scheduling problem with weighted tardiness penalty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 89-101, January.
    6. Lei, Deming & Guo, Xiuping, 2015. "A parallel neighborhood search for order acceptance and scheduling in flow shop environment," International Journal of Production Economics, Elsevier, vol. 165(C), pages 12-18.
    7. Wang, Xiuli & Zhu, Qianqian & Cheng, T.C.E., 2015. "Subcontracting price schemes for order acceptance and scheduling," Omega, Elsevier, vol. 54(C), pages 1-10.
    8. Tarhan, İstenç & Oğuz, Ceyda, 2022. "A matheuristic for the generalized order acceptance and scheduling problem," European Journal of Operational Research, Elsevier, vol. 299(1), pages 87-103.
    9. Naderi, Bahman & Roshanaei, Vahid, 2020. "Branch-Relax-and-Check: A tractable decomposition method for order acceptance and identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 286(3), pages 811-827.
    10. Joonyup Eun & Chang Sup Sung & Eun-Seok Kim, 2017. "Maximizing total job value on a single machine with job selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 998-1005, September.

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