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Bounds for parallel machine scheduling with predefined parts of jobs and setup time

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  • Hyun-Jung Kim

    (Sungkyunkwan University)

Abstract

We examine a parallel machine scheduling problem with setup time in order to minimize the makespan. Each job consists of predefined parts, and the parts of a job can be processed concurrently in different machines. We first provide worst-case bounds of the Longest Processing Time and list schedules for special cases. We also develop a heuristic algorithm for scheduling parts of jobs with family setup time and propose its worst-case performance ratio. The application is the IoT-based smart factory with 3D printers as processing machines.

Suggested Citation

  • Hyun-Jung Kim, 2018. "Bounds for parallel machine scheduling with predefined parts of jobs and setup time," Annals of Operations Research, Springer, vol. 261(1), pages 401-412, February.
    • Handle: RePEc:spr:annopr:v:261:y:2018:i:1:d:10.1007_s10479-017-2615-z
    DOI: 10.1007/s10479-017-2615-z
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    References listed on IDEAS

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    1. Navid Hashemian & Claver Diallo & Béla Vizvári, 2014. "Makespan minimization for parallel machines scheduling with multiple availability constraints," Annals of Operations Research, Springer, vol. 213(1), pages 173-186, February.
    2. Clyde L. Monma & Chris N. Potts, 1993. "Analysis of Heuristics for Preemptive Parallel Machine Scheduling with Batch Setup Times," Operations Research, INFORMS, vol. 41(5), pages 981-993, October.
    3. Ali Obeid & Stéphane Dauzère-Pérès & Claude Yugma, 2014. "Scheduling job families on non-identical parallel machines with time constraints," Annals of Operations Research, Springer, vol. 213(1), pages 221-234, February.
    4. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    5. Nait Tahar, Djamel & Yalaoui, Farouk & Chu, Chengbin & Amodeo, Lionel, 2006. "A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times," International Journal of Production Economics, Elsevier, vol. 99(1-2), pages 63-73, February.
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    Cited by:

    1. Zehetner, Dominik & Gansterer, Margaretha, 2022. "The collaborative batching problem in multi-site additive manufacturing," International Journal of Production Economics, Elsevier, vol. 248(C).
    2. Mecler, Davi & Abu-Marrul, Victor & Martinelli, Rafael & Hoff, Arild, 2022. "Iterated greedy algorithms for a complex parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 300(2), pages 545-560.
    3. Zhe Zhang & Xiaoling Song & Huijun Huang & Yong Yin & Benjamin Lev, 2022. "Scheduling problem in seru production system considering DeJong’s learning effect and job splitting," Annals of Operations Research, Springer, vol. 312(2), pages 1119-1141, May.
    4. Jun-Ho Lee & Hyun-Jung Kim, 2021. "A heuristic algorithm for identical parallel machine scheduling: splitting jobs, sequence-dependent setup times, and limited setup operators," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 992-1026, December.
    5. Bentao Su & Naiming Xie, 2020. "Single workgroup scheduling problem with variable processing personnel," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 671-684, June.

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