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Job-Shop Scheduling Theory: What Is Relevant?

Author

Listed:
  • Kenneth N. McKay

    (Department of Management Sciences, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1)

  • Frank R. Safayeni

    (Department of Management Sciences, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1)

  • John A. Buzacott

    (Department of Management Sciences, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1)

Abstract

The theoretical approach of OR and AI to scheduling often is not applicable to the dynamic characteristics of the actual situation. A preliminary field study is used to illustrate that the basic theoretical approach does not represent the reality of open job-shop scheduling, and its applicability is limited to those situations that are fundamentally static and behave like the models. Better understanding and modeling of the scheduling situation is needed.

Suggested Citation

  • Kenneth N. McKay & Frank R. Safayeni & John A. Buzacott, 1988. "Job-Shop Scheduling Theory: What Is Relevant?," Interfaces, INFORMS, vol. 18(4), pages 84-90, August.
    • Handle: RePEc:inm:orinte:v:18:y:1988:i:4:p:84-90
    DOI: 10.1287/inte.18.4.84
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    Citations

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    Cited by:

    1. Koltai, Tamás, 2009. "Robustness of a production schedule to inventory cost calculations," International Journal of Production Economics, Elsevier, vol. 121(2), pages 494-504, October.
    2. P J Kalczynski & J Kamburowski, 2004. "Generalization of Johnson's and Talwar's scheduling rules in two-machine stochastic flow shops," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1358-1362, December.
    3. Durk-Jouke van der Zee, 2017. "Coordinating batching decisions in manufacturing networks," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5405-5422, September.
    4. De', Rahul & May, Jerrold H, 1998. "Using Operational Planning Horizons for Determining Setup Changes," Omega, Elsevier, vol. 26(5), pages 581-592, October.
    5. Wiers, V. C. S., 1997. "A review of the applicability of OR and AI scheduling techniques in practice," Omega, Elsevier, vol. 25(2), pages 145-153, April.
    6. Framinan, Jose M. & Ruiz, Rubén, 2010. "Architecture of manufacturing scheduling systems: Literature review and an integrated proposal," European Journal of Operational Research, Elsevier, vol. 205(2), pages 237-246, September.
    7. Victor Portougal & David J. Robb, 2000. "Production Scheduling Theory: Just Where Is It Applicable?," Interfaces, INFORMS, vol. 30(6), pages 64-76, December.
    8. Soroush, H. M., 1999. "Sequencing and due-date determination in the stochastic single machine problem with earliness and tardiness costs," European Journal of Operational Research, Elsevier, vol. 113(2), pages 450-468, March.
    9. Ruiz, Ruben & Maroto, Concepcion, 2006. "A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility," European Journal of Operational Research, Elsevier, vol. 169(3), pages 781-800, March.
    10. 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.
    11. Subhash C. Sarin & Balaji Nagarajan & Sanjay Jain & Lingrui Liao, 2009. "Analytic evaluation of the expectation and variance of different performance measures of a schedule on a single machine under processing time variability," Journal of Combinatorial Optimization, Springer, vol. 17(4), pages 400-416, May.
    12. Marco Wurster & Marius Michel & Marvin Carl May & Andreas Kuhnle & Nicole Stricker & Gisela Lanza, 2022. "Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 575-591, February.
    13. Ilkyeong Moon & Sanghyup Lee & Moonsoo Shin & Kwangyeol Ryu, 2016. "Evolutionary resource assignment for workload-based production scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 375-388, April.
    14. Mohamed Kriouich & Hicham Sarir, 2024. "Artificial Intelligence Application in Production Scheduling Problem Systematic Literature Review: Bibliometric Analysis, Research Trend, and Knowledge Taxonomy," SN Operations Research Forum, Springer, vol. 5(2), pages 1-24, June.
    15. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.

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