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The “Largest Variance First” Policy in Some Stochastic Scheduling Problems

Author

Listed:
  • Michael Pinedo

    (Columbia University, New York, New York)

  • Gideon Weiss

    (Georgia Institute of Technology, Atlanta, Georgia)

Abstract

We consider a situation in which n jobs, requiring random amounts of processing, all with the same mean, are to be scheduled on m parallel machines with respect to one of two objectives: expected flowtime and expected makespan. We discuss optimality of the rule that says to schedule the jobs with the largest variance first (LVF). We show that for some very simple job length distributions, LVF minimizes both the expected flowtime and the expected makespan.

Suggested Citation

  • Michael Pinedo & Gideon Weiss, 1987. "The “Largest Variance First” Policy in Some Stochastic Scheduling Problems," Operations Research, INFORMS, vol. 35(6), pages 884-891, December.
    • Handle: RePEc:inm:oropre:v:35:y:1987:i:6:p:884-891
    DOI: 10.1287/opre.35.6.884
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    Cited by:

    1. Janis Brammer & Bernhard Lutz & Dirk Neumann, 2022. "Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 29-56, March.

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