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Stochastic Online Scheduling

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  • Vredeveld, T.

    (Quantitative Economics)

Abstract

In this paper we consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Jobs arrive in an online manner and as soon as a job becomes known, the scheduler only learns about the probability distribution of the processing time and not the actual processing time. This model is called the stochastic online scheduling (SOS) model. Both online scheduling and stochastic scheduling are special cases of this model. In this paper, we survey the results for the SOS model.

Suggested Citation

  • Vredeveld, T., 2009. "Stochastic Online Scheduling," Research Memorandum 052, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    • Handle: RePEc:unm:umamet:2009052
    DOI: 10.26481/umamet.2009052
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    References listed on IDEAS

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    3. Nicole Megow & Marc Uetz & Tjark Vredeveld, 2006. "Models and Algorithms for Stochastic Online Scheduling," Mathematics of Operations Research, INFORMS, vol. 31(3), pages 513-525, August.
    4. Sousa, J. & Wolsey, L., 1989. "Time Indexed Formulations Of Non-Preemptive Single-Machine Schduling Problems," LIDAM Discussion Papers CORE 1989004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Wayne E. Smith, 1956. "Various optimizers for single‐stage production," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 59-66, March.
    6. Anja Feldmann & Ming-Yang Kao & Jiří Sgall & Shang-Hua Teng, 1998. "Optimal On-Line Scheduling of Parallel Jobs with Dependencies," Journal of Combinatorial Optimization, Springer, vol. 1(4), pages 393-411, December.
    7. Megow, N. & Vredeveld, T., 2006. "Approximation results for preemptive stochastic online scheduling," Research Memorandum 053, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
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    1. Megow, N. & Vredeveld, T., 2009. "Approximating preemptive stochastic scheduling," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

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