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Auction Design for the Efficient Allocation of Service Capacity Under Congestion

Author

Listed:
  • Jorge Barrera

    (Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, 22904)

  • Alfredo Garcia

    (Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, 32611)

Abstract

We consider the problem of efficiently allocating the capacity of a number of service facilities (prone to congestion) to a set of users with private information regarding their willingness to pay for different combinations of throughput versus latency. Auction mechanisms can be used to schedule the service capacity of available facilities. However, the interdependency of users’ valuations implies that simple uniform price adjustment processes (e.g., tatonnement) either fail to effectively clear or are subject to strategic manipulation. In this paper, we propose an iterative auction design and show that (i) it is efficient (i.e., the auction closes with the allocation of service that maximizes the social welfare) and (ii) it is strategy-proof, that is, it is a dominant strategy for users to truthfully reveal their demand for service capacity throughout the auction.

Suggested Citation

  • Jorge Barrera & Alfredo Garcia, 2015. "Auction Design for the Efficient Allocation of Service Capacity Under Congestion," Operations Research, INFORMS, vol. 63(1), pages 151-165, February.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:1:p:151-165
    DOI: 10.1287/opre.2014.1333
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    References listed on IDEAS

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    Cited by:

    1. Minghui Lai & Weili Xue & Qian Hu, 2019. "An Ascending Auction for Freight Forwarder Collaboration in Capacity Sharing," Transportation Science, INFORMS, vol. 53(4), pages 1175-1195, July.
    2. Lai, Minghui & Cai, Xiaoqiang & Hu, Qian, 2017. "An iterative auction for carrier collaboration in truckload pickup and delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 60-80.
    3. Zhong-Ping Li & Jian-Jun Wang & Ai-Chih Chang & Jim Shi, 2021. "Capacity reallocation via sinking high-quality resource in a hierarchical healthcare system," Annals of Operations Research, Springer, vol. 300(1), pages 97-135, May.

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