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Scheduling of Multi-Class Single-Server Queues Under Nontraditional Performance Measures

Author

Listed:
  • Hayriye Ayhan

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205)

  • Tava Lennon Olsen

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, 48109-2117)

Abstract

We consider a multi-class production system without setups where many job classes share a single server. The traditional performance measure used for scheduling these systems is that of mean throughput time (i.e., the time spent in the system). However, mean throughput time may not be the only measure of importance in real systems. In particular, throughput time variance and the outer percentiles of throughput time may be equally important. We present two heuristics for scheduling multi-class single-server queues that are based on heavy-traffic analysis and perform well with respect to these nontraditional measures in a wide variety of cases. An approximation is given for the throughput time distribution under both scheduling methods.

Suggested Citation

  • Hayriye Ayhan & Tava Lennon Olsen, 2000. "Scheduling of Multi-Class Single-Server Queues Under Nontraditional Performance Measures," Operations Research, INFORMS, vol. 48(3), pages 482-489, June.
    • Handle: RePEc:inm:oropre:v:48:y:2000:i:3:p:482-489
    DOI: 10.1287/opre.48.3.482.12428
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    References listed on IDEAS

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    Cited by:

    1. Jan A. Van Mieghem, 2003. "Due-Date Scheduling: Asymptotic Optimality of Generalized Longest Queue and Generalized Largest Delay Rules," Operations Research, INFORMS, vol. 51(1), pages 113-122, February.
    2. Jan A. Van Mieghem, 2000. "Price and Service Discrimination in Queuing Systems: Incentive Compatibility of Gc\mu Scheduling," Management Science, INFORMS, vol. 46(9), pages 1249-1267, September.
    3. 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.
    4. 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.
    5. Barış Ata & Tava Lennon Olsen, 2009. "Near-Optimal Dynamic Lead-Time Quotation and Scheduling Under Convex-Concave Customer Delay Costs," Operations Research, INFORMS, vol. 57(3), pages 753-768, June.

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