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Scheduling to Minimize Average Completion Time Revisited: Deterministic On-line Algorithms

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  • Megow, Nicole
  • Schulz, Andreas S.

Abstract

We consider the scheduling problem of minimizing the average weighted completion time on identical parallel machines when jobs are arriving over time. For both the preemptive and the nonpreemptive setting, we show that straightforward extensions of Smith's ratio rule yield smaller competitive ratios compared to the previously best-known deterministic on-line algorithms, which are (4+epsilon)-competitive in either case. Our preemptive algorithm is 2-competitive, which actually meets the competitive ratio of the currently best randomized on-line algorithm for this scenario. Our nonpreemptive algorithm has a competitive ratio of 3.28. Both results are characterized by a surprisingly simple analysis; moreover, the preemptive algorithm also works in the less clairvoyant environment in which only the ratio of weight to processing time of a job becomes known at its release date, but neither its actual weight nor its processing time. In the corresponding nonpreemptive situation, every on-line algorithm has an unbounded competitive ratio

Suggested Citation

  • Megow, Nicole & Schulz, Andreas S., 2004. "Scheduling to Minimize Average Completion Time Revisited: Deterministic On-line Algorithms," Working papers 4435-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    • Handle: RePEc:mit:sloanp:4048
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    File URL: http://hdl.handle.net/1721.1/4048
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    References listed on IDEAS

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    1. Leslie A. Hall & Andreas S. Schulz & David B. Shmoys & Joel Wein, 1997. "Scheduling to Minimize Average Completion Time: Off-Line and On-Line Approximation Algorithms," Mathematics of Operations Research, INFORMS, vol. 22(3), pages 513-544, August.
    2. W. L. Eastman & S. Even & I. M. Isaacs, 1964. "Bounds for the Optimal Scheduling of n Jobs on m Processors," Management Science, INFORMS, vol. 11(2), pages 268-279, November.
    3. Linus Schrage, 1968. "Letter to the Editor—A Proof of the Optimality of the Shortest Remaining Processing Time Discipline," Operations Research, INFORMS, vol. 16(3), pages 687-690, June.
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    Cited by:

    1. Christopher Dance & Alexei Gaivoronski, 2012. "Stochastic optimization for real time service capacity allocation under random service demand," Annals of Operations Research, Springer, vol. 193(1), pages 221-253, March.

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