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Mechanism Design for Decentralized Online Machine Scheduling

Author

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  • Birgit Heydenreich

    (Department of Quantitative Economics, Maastricht University, 6200 MD Maastricht, The Netherlands)

  • Rudolf Müller

    (Department of Quantitative Economics, Maastricht University, 6200 MD Maastricht, The Netherlands)

  • Marc Uetz

    (Department of Applied Mathematics, University of Twente, 7500 AE Enschede, The Netherlands)

Abstract

Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents make autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to suboptimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best-response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that---assuming rational behavior of jobs---results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only slightly worse than state-of-the-art algorithms with central coordination.

Suggested Citation

  • Birgit Heydenreich & Rudolf Müller & Marc Uetz, 2010. "Mechanism Design for Decentralized Online Machine Scheduling," Operations Research, INFORMS, vol. 58(2), pages 445-457, April.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:2:p:445-457
    DOI: 10.1287/opre.1090.0732
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    References listed on IDEAS

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    Cited by:

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    2. Jie Yang & Fang He & Xi Lin & Max Zuo‐Jun Shen, 2021. "Mechanism Design for Stochastic Dynamic Parking Resource Allocation," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3615-3634, October.

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