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Multi-Agent Customer Allocation in a Stochastic Service System

Author

Listed:
  • Hau Leung Lee

    (Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305)

  • Morris A. Cohen

    (Department of Decision Sciences, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

In many service systems, customers interact with an agent who directs customers to specific service facilities. Each agent, as a decision maker, seeks to allocate his/her customers to the service centers so as to optimize a measure of performance based on the customers' expected waiting time and the expected number of customers in service. In this paper, the problem of multiple agents, each optimizing his/her customer allocation decision in a stochastic service system, is analyzed as a noncooperative game. It is shown that an equilibrium point to such a game exists and sufficient conditions for which this equilibrium point is unique are also given. Finally, the relative efficiency of the multi-agent system is examined by comparing the customers' average waiting time in the multi-agent system to the one-agent case. It is shown that, in general, the multi-agent system is not as efficient as the one-agent one in terms of customer welfare.

Suggested Citation

  • Hau Leung Lee & Morris A. Cohen, 1985. "Multi-Agent Customer Allocation in a Stochastic Service System," Management Science, INFORMS, vol. 31(6), pages 752-763, June.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:6:p:752-763
    DOI: 10.1287/mnsc.31.6.752
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    Citations

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    Cited by:

    1. Gupta, Alok & Stahl, Dale O. & Whinston, Andrew B., 1997. "A stochastic equilibrium model of internet pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(4-5), pages 697-722, May.
    2. Nicos Savva & Tolga Tezcan & Özlem Yıldız, 2019. "Can Yardstick Competition Reduce Waiting Times?," Management Science, INFORMS, vol. 65(7), pages 3196-3215, July.
    3. Humoud Alsabah & Benjamin Bernard & Agostino Capponi & Garud Iyengar & Jay Sethuraman, 2021. "Multiregional Oligopoly with Capacity Constraints," Management Science, INFORMS, vol. 67(8), pages 4789-4808, August.
    4. Grossman, Thomas A. & Brandeau, Margaret L., 2002. "Optimal pricing for service facilities with self-optimizing customers," European Journal of Operational Research, Elsevier, vol. 141(1), pages 39-57, August.
    5. Vishal Ahuja & Carlos A. Alvarez & Bradley R. Staats, 2020. "Maintaining Continuity in Service: An Empirical Examination of Primary Care Physicians," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1088-1106, September.
    6. Xiuli Chao & Liming Liu & Shaohui Zheng, 2003. "Resource Allocation in Multisite Service Systems with Intersite Customer Flows," Management Science, INFORMS, vol. 49(12), pages 1739-1752, December.
    7. Gad Allon & Awi Federgruen, 2009. "Competition in Service Industries with Segmented Markets," Management Science, INFORMS, vol. 55(4), pages 619-634, April.
    8. Eitan Altman & Nahum Shimkin, 1998. "Individual Equilibrium and Learning in Processor Sharing Systems," Operations Research, INFORMS, vol. 46(6), pages 776-784, December.
    9. Robert A. Shumsky & Edieal J. Pinker, 2003. "Gatekeepers and Referrals in Services," Management Science, INFORMS, vol. 49(7), pages 839-856, July.
    10. Albert Y. Ha, 2001. "Optimal Pricing That Coordinates Queues with Customer-Chosen Service Requirements," Management Science, INFORMS, vol. 47(7), pages 915-930, July.
    11. Dale Stahl, "undated". "Pricing for Electronic Commerce," Computing in Economics and Finance 1996 _054, Society for Computational Economics.

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