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Unrelated parallel machine scheduling problem with stochastic sequence dependent setup times

Author

Listed:
  • Tugba Saraç

    (Eskisehir Osmangazi University)

  • Feristah Ozcelik

    (Eskisehir Osmangazi University)

  • Mehmet Ertem

    (Eskisehir Osmangazi University)

Abstract

Unrelated parallel machine scheduling problem (UPM) is widely studied in the scheduling literature because of its extensive application area in the industry. Since it has a stochastic nature, several studies handled the problem as stochastic. However, most of the studies that have considered the problem as stochastic focused only on the case of stochastic processing times. Whereas, especially in industries where setup times are sequence and machine-dependent, these are often stochastic, as well. Although this situation has been ignored in the literature for a long time, it has been examined only in a few studies. In this study, for the first time, an exact solution method is proposed to solve UPM with stochastic sequence-dependent setup times (SDSTs). For the considered problem, a two-stage stochastic programming method is proposed. A mathematical model and a genetic algorithm are developed for the stochastic problem. The effectiveness of the proposed solution approaches is demonstrated using randomly generated test problems. The test results demonstrate the importance of considering the SDSTs as stochastic.

Suggested Citation

  • Tugba Saraç & Feristah Ozcelik & Mehmet Ertem, 2023. "Unrelated parallel machine scheduling problem with stochastic sequence dependent setup times," Operational Research, Springer, vol. 23(3), pages 1-19, September.
    • Handle: RePEc:spr:operea:v:23:y:2023:i:3:d:10.1007_s12351-023-00789-3
    DOI: 10.1007/s12351-023-00789-3
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    References listed on IDEAS

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    1. Chang, Zhiqi & Song, Shiji & Zhang, Yuli & Ding, Jian-Ya & Zhang, Rui & Chiong, Raymond, 2017. "Distributionally robust single machine scheduling with risk aversion," European Journal of Operational Research, Elsevier, vol. 256(1), pages 261-274.
    2. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    3. Ali Salmasnia & Mostafa Khatami & Reza Kazemzadeh & Seyed Zegordi, 2015. "Bi-objective single machine scheduling problem with stochastic processing times," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 275-297, April.
    4. Xin Liu & Feng Chu & Feifeng Zheng & Chengbin Chu & Ming Liu, 2021. "Parallel machine scheduling with stochastic release times and processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 59(20), pages 6327-6346, October.
    5. Mehmet Ertem & Feristah Ozcelik & Tugba Saraç, 2019. "Single machine scheduling problem with stochastic sequence-dependent setup times," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3273-3289, May.
    6. Semih Atakan & Kerem Bülbül & Nilay Noyan, 2017. "Minimizing value-at-risk in single-machine scheduling," Annals of Operations Research, Springer, vol. 248(1), pages 25-73, January.
    7. Novak, Antonin & Sucha, Premysl & Novotny, Matej & Stec, Richard & Hanzalek, Zdenek, 2022. "Scheduling jobs with normally distributed processing times on parallel machines," European Journal of Operational Research, Elsevier, vol. 297(2), pages 422-441.
    8. Baker, Kenneth R., 2014. "Minimizing earliness and tardiness costs in stochastic scheduling," European Journal of Operational Research, Elsevier, vol. 236(2), pages 445-452.
    9. de Weerdt, Mathijs & Baart, Robert & He, Lei, 2021. "Single-machine scheduling with release times, deadlines, setup times, and rejection," European Journal of Operational Research, Elsevier, vol. 291(2), pages 629-639.
    10. Rajendran, Chandrasekharan & Ziegler, Hans, 2003. "Scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 149(3), pages 513-522, September.
    11. Lei Xu & Qian Wang & Simin Huang, 2015. "Dynamic order acceptance and scheduling problem with sequence-dependent setup time," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5797-5808, October.
    12. Lemos, R.F. & Ronconi, D.P., 2015. "Heuristics for the stochastic single-machine problem with E/T costs," International Journal of Production Economics, Elsevier, vol. 168(C), pages 131-142.
    13. Jinwei Gu & Manzhan Gu & Xingsheng Gu, 2014. "Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, September.
    14. Varun Gupta & Benjamin Moseley & Marc Uetz & Qiaomin Xie, 2020. "Greed Works—Online Algorithms for Unrelated Machine Stochastic Scheduling," Mathematics of Operations Research, INFORMS, vol. 45(2), pages 497-516, May.
    15. Anupam Gupta & Amit Kumar & Viswanath Nagarajan & Xiangkun Shen, 2021. "Stochastic Load Balancing on Unrelated Machines," Mathematics of Operations Research, INFORMS, vol. 46(1), pages 115-133, February.
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