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Obtaining Fast Service in a Queueing System via Performance-Based Allocation of Demand


  • Gérard P. Cachon

    () (Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Fuqiang Zhang

    () (Paul Merage School of Business, University of California, Irvine, California 92697-3125)


Any buyer that depends on suppliers for the delivery of a service or the production of a make-to-order component should pay close attention to the suppliers' service or delivery lead times. This paper studies a queueing model in which two strategic servers choose their capacities/processing rates and faster service is costly. The buyer allocates demand to the servers based on their performance; the faster a server works, the more demand the server is allocated. The buyer's objective is to minimize the average lead time received from the servers. There are two important attributes to consider in the design of an allocation policy: the degree to which the allocation policy effectively utilizes the servers' capacities and the strength of the incentives the allocation policy provides for the servers to work quickly. Previous research suggests that there exists a trade-off between efficiency and incentives, i.e., in the choice between two allocation policies a buyer may prefer the less efficient one because it provides stronger incentives. We find considerable variation in the performance of allocation policies: Some intuitively reasonable policies generate essentially no competition among servers to work quickly, whereas others generate too much competition, thereby causing some servers to refuse to work with the buyer. Nevertheless, the trade-off between efficiency and incentives need not exist: It is possible to design an allocation policy that is efficient and also induces the servers to work quickly. We conclude that performance-based allocation can be an effective procurement strategy for a buyer as long as the buyer explicitly accounts for the servers' strategic behavior.

Suggested Citation

  • Gérard P. Cachon & Fuqiang Zhang, 2007. "Obtaining Fast Service in a Queueing System via Performance-Based Allocation of Demand," Management Science, INFORMS, vol. 53(3), pages 408-420, March.
  • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:3:p:408-420

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    References listed on IDEAS

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    1. Sarang Deo & Itai Gurvich, 2011. "Centralized vs. Decentralized Ambulance Diversion: A Network Perspective," Management Science, INFORMS, pages 1300-1319.
    2. Lauren Xiaoyuan Lu & Jan A. Van Mieghem & R. Canan Savaskan, 2009. "Incentives for Quality Through Endogenous Routing," Manufacturing & Service Operations Management, INFORMS, pages 254-273.
    3. Gad Allon & Achal Bassamboo & Eren B. Çil, 2012. "Large-Scale Service Marketplaces: The Role of the Moderating Firm," Management Science, INFORMS, pages 1854-1872.
    4. Cuihong Li & Laurens G. Debo, 2009. "Second Sourcing vs. Sole Sourcing with Capacity Investment and Asymmetric Information," Manufacturing & Service Operations Management, INFORMS, pages 448-470.
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    6. H. Dharma Kwon & Steven A. Lippman & Kevin F. McCardle & Christopher S. Tang, 2010. "Project Management Contracts with Delayed Payments," Manufacturing & Service Operations Management, INFORMS, pages 692-707.
    7. Weixin Shang & Liming Liu, 2011. "Promised Delivery Time and Capacity Games in Time-Based Competition," Management Science, INFORMS, pages 599-610.
    8. Volodymyr Babich & Apostolos N. Burnetas & Peter H. Ritchken, 2007. "Competition and Diversification Effects in Supply Chains with Supplier Default Risk," Manufacturing & Service Operations Management, INFORMS, pages 123-146.
    9. Anton, James J. & Brusco, Sandro & Lopomo, Giuseppe, 2010. "Split-award procurement auctions with uncertain scale economies: Theory and data," Games and Economic Behavior, Elsevier, pages 24-41.
    10. Li, Yung-Ming & Lee, Yi-Lin, 2010. "Pricing peer-produced services: Quality, capacity, and competition issues," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1658-1668, December.
    11. Lauren Xiaoyuan Lu & Martin A. Lariviere, 2012. "Capacity Allocation over a Long Horizon: The Return on Turn-and-Earn," Manufacturing & Service Operations Management, INFORMS, pages 24-41.
    12. repec:pal:jorsoc:v:68:y:2017:i:6:d:10.1057_s41274-016-0100-8 is not listed on IDEAS
    13. Sameer Hasija & Edieal Pinker & Robert A. Shumsky, 2010. "OM Practice--Work Expands to Fill the Time Available: Capacity Estimation and Staffing Under Parkinson's Law," Manufacturing & Service Operations Management, INFORMS, pages 1-18.
    14. Qi, Lian & Shi, Jim (Junmin) & Xu, Xiaowei, 2015. "Supplier competition and its impact on firm׳s sourcing strategy," Omega, Elsevier, vol. 55(C), pages 91-110.
    15. Elahi, Ehsan, 2013. "Outsourcing through competition: What is the best competition parameter?," International Journal of Production Economics, Elsevier, vol. 144(1), pages 370-382.
    16. Jain, Tarun & Hazra, Jishnu, 2017. "Dual sourcing under suppliers' capacity investments," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 103-115.
    17. Daniel Adelman & Adam J. Mersereau, 2013. "Dynamic Capacity Allocation to Customers Who Remember Past Service," Management Science, INFORMS, pages 592-612.


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