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Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches

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  • Shioura, Akiyoshi
  • Shakhlevich, Natalia V.
  • Strusevich, Vitaly A.

Abstract

This paper provides a review of recent results on scheduling with controllable processing times. The stress is on the methodological aspects that include parametric flow techniques and methods for solving mathematical programming problems with submodular constraints. We show that the use of these methodologies yields fast algorithms for solving problems on single machine or parallel machines, with either one or several objective functions. For a wide range of problems with controllable processing times we report algorithms with the running times which match those known for the corresponding problems with fixed processing times. As a by-product, we present the best possible algorithms for a number of problems on parallel machines that are traditionally studied within the body of research on scheduling with imprecise computation.

Suggested Citation

  • Shioura, Akiyoshi & Shakhlevich, Natalia V. & Strusevich, Vitaly A., 2018. "Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches," European Journal of Operational Research, Elsevier, vol. 266(3), pages 795-818.
    • Handle: RePEc:eee:ejores:v:266:y:2018:i:3:p:795-818
    DOI: 10.1016/j.ejor.2017.08.034
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    References listed on IDEAS

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    Cited by:

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    3. Akiyoshi Shioura & Natalia V. Shakhlevich & Vitaly A. Strusevich, 2020. "Scheduling problems with controllable processing times and a common deadline to minimize maximum compression cost," Journal of Global Optimization, Springer, vol. 76(3), pages 471-490, March.
    4. Chen, Xin & Liang, Yage & Sterna, Małgorzata & Wang, Wen & Błażewicz, Jacek, 2020. "Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date," European Journal of Operational Research, Elsevier, vol. 284(1), pages 67-74.
    5. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    6. Shi-Sheng Li & Ren-Xia Chen, 2022. "Minimizing total weighted late work on a single-machine with non-availability intervals," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1330-1355, September.
    7. Stanisław Gawiejnowicz, 2020. "A review of four decades of time-dependent scheduling: main results, new topics, and open problems," Journal of Scheduling, Springer, vol. 23(1), pages 3-47, February.
    8. Lingfa Lu & Liqi Zhang & Jinwen Ou, 2021. "In-house production and outsourcing under different discount schemes on the total outsourcing cost," Annals of Operations Research, Springer, vol. 298(1), pages 361-374, March.
    9. Castro, Pedro M. & Harjunkoski, Iiro & Grossmann, Ignacio E., 2019. "Discrete and continuous-time formulations for dealing with break periods: Preemptive and non-preemptive scheduling," European Journal of Operational Research, Elsevier, vol. 278(2), pages 563-577.
    10. Kramer, Arthur & Dell’Amico, Mauro & Iori, Manuel, 2019. "Enhanced arc-flow formulations to minimize weighted completion time on identical parallel machines," European Journal of Operational Research, Elsevier, vol. 275(1), pages 67-79.
    11. Shi-Sheng Li & Jin-Jiang Yuan, 2020. "Single-machine scheduling with multi-agents to minimize total weighted late work," Journal of Scheduling, Springer, vol. 23(4), pages 497-512, August.

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