Stochastic Resource Allocation Problems: Minmax and Maxmin Solutions

Anh Ninh () and Zuo-Jun Max Shen ()
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Anh Ninh: Department of Mathematics, William & Mary, Williamsburg, Virginia 23185
Zuo-Jun Max Shen: Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720;; Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720

Manufacturing & Service Operations Management, 2024, vol. 26, issue 6, 2322-2335

Abstract: Problem definition : Min-max or max-min objective criteria naturally arise in many resource allocation applications, especially when the attendant goal involves distribution of a limited resource across multiple subsystems, locations, or activities. Each has a different consumption rate of the resource. In this setting, the decision maker is interested in optimizing the system performance, which can be measured in terms of the smallest or largest order statistic across subsystems. Examples include the allocation of health resources to maximize the first runout time or to minimize the maximum delay. Methodology/results : In the literature, there are no unified theoretical and computational results on the optimal allocations for these problems. Thus, in this paper, we develop key theoretical properties of the attendant stochastic resource allocation problems, providing a technical condition for the objective functions to have favorable properties, upon which we derive novel optimal allocation rules with clear interpretation. Managerial implications : The proportional allocation rule can be interpreted as a robust solution when the full distribution of the interarrival time is unknown. It generally has poor performance, especially when the resource consumption rates vary across locations, which happens regularly in practice. Our allocation rule is exact and should be used for the allocation of scarce resources—that is, when the number of resources per location is limited, which is often encountered in healthcare settings.

Keywords: stochastic allocation; order statistics; log-concave; clinical trials; recruitment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:6:p:2322-2335

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