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Adaptive Routing on the Plane

Author

Listed:
  • Leandros Tassiulas

    (University of Maryland, College Park, Maryland)

Abstract

Demands for service arrive at random times, in random locations, in a region of the plane. The service time of each demand is random. A server that travels with constant speed moves from demand to demand providing service. The server spends its time either in providing service or in traveling. The objective is to route the server, based on the location of the current demands on the plane and the anticipated demand arrivals, such that the time spent in traveling is minimal and the service is provided efficiently. A routing policy is provided that achieves maximum throughput and is independent of the statistical parameters of the system, under the assumption that the arrival process is Poisson. For a renewal arrival process, a class of policies is specified that achieve maximum throughput based on some knowledge of the system parameters. Finally, an adaptive version of the partitioning policies is given, which makes them independent of the system statistics.

Suggested Citation

  • Leandros Tassiulas, 1996. "Adaptive Routing on the Plane," Operations Research, INFORMS, vol. 44(5), pages 823-832, October.
    • Handle: RePEc:inm:oropre:v:44:y:1996:i:5:p:823-832
    DOI: 10.1287/opre.44.5.823
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    Cited by:

    1. Marlin W. Ulmer & Dirk C. Mattfeld & Felix Köster, 2018. "Budgeting Time for Dynamic Vehicle Routing with Stochastic Customer Requests," Transportation Science, INFORMS, vol. 52(1), pages 20-37, January.
    2. Zhang, Jian & Luo, Kelin & Florio, Alexandre M. & Van Woensel, Tom, 2023. "Solving large-scale dynamic vehicle routing problems with stochastic requests," European Journal of Operational Research, Elsevier, vol. 306(2), pages 596-614.
    3. Soeffker, Ninja & Ulmer, Marlin W. & Mattfeld, Dirk C., 2022. "Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review," European Journal of Operational Research, Elsevier, vol. 298(3), pages 801-820.
    4. Marlin W. Ulmer & Justin C. Goodson & Dirk C. Mattfeld & Marco Hennig, 2019. "Offline–Online Approximate Dynamic Programming for Dynamic Vehicle Routing with Stochastic Requests," Service Science, INFORMS, vol. 53(1), pages 185-202, February.

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