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A Distributed Decision-Making Structure for Dynamic Resource Allocation Using Nonlinear Functional Approximations

Author

Listed:
  • Huseyin Topaloglu

    (School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853)

  • Warren B. Powell

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544)

Abstract

This paper proposes a distributed solution approach to a certain class of dynamic resource allocation problems and develops a dynamic programming-based multiagent decision-making, learning, and communication mechanism. In the class of dynamic resource allocation problems we consider, a set of reusable resources of different types has to be assigned to tasks that arrive randomly over time. The assignment of a resource to a task removes the task from the system, modifies the state of the resource, and generates a contribution. We build a decision-making scheme where the decisions regarding the resources in different sets of states are made by different agents. We explain how to coordinate the actions of different agents using nonlinear functional approximations, and show that in a distributed setting, nonlinear approximations produce sequences of min-cost network flow problems that naturally yield integer solutions. We also experimentally compare the performances of the centralized and distributed solution strategies.

Suggested Citation

  • Huseyin Topaloglu & Warren B. Powell, 2005. "A Distributed Decision-Making Structure for Dynamic Resource Allocation Using Nonlinear Functional Approximations," Operations Research, INFORMS, vol. 53(2), pages 281-297, April.
    • Handle: RePEc:inm:oropre:v:53:y:2005:i:2:p:281-297
    DOI: 10.1287/opre.1040.0166
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    References listed on IDEAS

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    1. Gregory A. Godfrey & Warren B. Powell, 2001. "An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution," Management Science, INFORMS, vol. 47(8), pages 1101-1112, August.
    2. D. P. de Farias & B. Van Roy, 2003. "The Linear Programming Approach to Approximate Dynamic Programming," Operations Research, INFORMS, vol. 51(6), pages 850-865, December.
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    Cited by:

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    2. Daniel F. Salas & Warren B. Powell, 2018. "Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 106-123, February.
    3. Archis Ghate & Robert L. Smith, 2013. "A Linear Programming Approach to Nonstationary Infinite-Horizon Markov Decision Processes," Operations Research, INFORMS, vol. 61(2), pages 413-425, April.
    4. Gülpınar, Nalan & Çanakoğlu, Ethem & Branke, Juergen, 2018. "Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities," European Journal of Operational Research, Elsevier, vol. 266(1), pages 291-303.
    5. Cristián E. Cortés & Doris Sáez & Alfredo Núñez & Diego Muñoz-Carpintero, 2009. "Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 43(1), pages 27-42, February.
    6. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T. & Raoufi, R., 2014. "Multimodal freight transportation planning: A literature review," European Journal of Operational Research, Elsevier, vol. 233(1), pages 1-15.

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