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Network Dispatching by the Shortest-Operation Discipline

Author

Listed:
  • R. W. Conway

    (Department of Industrial and Engineering Administration, Cornell University, Ithaca, New York)

  • W. L. Maxwell

    (Department of Industrial and Engineering Administration, Cornell University, Ithaca, New York)

Abstract

The significance of the dispatching function in production planning and control is discussed and applicable results in sequencing and queuing theory are reviewed. Experimental results for a network of queues representing a small job shop are presented. The investigation involved the comparison of dispatching at random with dispatching in order of increasing processing time under different conditions of shop size, flow pattern, and level of work-in-process inventory. Also considered is the effect of imperfect a priori knowledge of processing times upon the shortest-operation discipline Several modifications of the shortest-operation discipline were also tested one in which the shortest-operation discipline is ‘truncated’ and another in which it is periodically alternated with a first-come-first-served discipline.

Suggested Citation

  • R. W. Conway & W. L. Maxwell, 1962. "Network Dispatching by the Shortest-Operation Discipline," Operations Research, INFORMS, vol. 10(1), pages 51-73, February.
    • Handle: RePEc:inm:oropre:v:10:y:1962:i:1:p:51-73
    DOI: 10.1287/opre.10.1.51
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    Cited by:

    1. Thiesse, Frédéric & Fleisch, Elgar, 2008. "On the value of location information to lot scheduling in complex manufacturing processes," International Journal of Production Economics, Elsevier, vol. 112(2), pages 532-547, April.
    2. Sabuncuoglu, Ihsan & Lejmi, Tahar, 1999. "Scheduling for non regular performance measure under the due window approach," Omega, Elsevier, vol. 27(5), pages 555-568, October.
    3. Douglas Toledo & Cristiane Akemi Umetsu & Antonio Fernando Monteiro Camargo & Idemauro Antonio Rodrigues Lara, 2022. "Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 473-497, September.
    4. Kasper, T.A. Arno & Land, Martin J. & Teunter, Ruud H., 2023. "Towards System State Dispatching in High‐Variety Manufacturing," Omega, Elsevier, vol. 114(C).
    5. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.
    6. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Hurtado, Margarita, 2018. "Exploiting the characteristics of serial queues to reduce the mean and variance of flow time using combined priority rules," International Journal of Production Economics, Elsevier, vol. 196(C), pages 211-225.

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