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A Restless Bandit Model for Resource Allocation, Competition and Reservation

We study a resource allocation problem with varying requests and with resources of limited capacity shared by multiple requests. It is modeled as a set of heterogeneous restless multiarmed bandit problems (RMABPs) connected by constraints imposed by resource capacity. Following Whittle’s relaxation idea and Weber and Weiss’ asymptotic optimality proof, we propose a simple policy and prove it to be asymptotically optimal in a regime where both arrival rates and capacities increase. We provide a simple sufficient condition for asymptotic optimality of the policy and, in complete generality, propose a method that generates a set of candidate policies for which asymptotic optimality can be checked. The effectiveness of these results is demonstrated by numerical experiments. To the best of our knowledge, this is the first work providing asymptotic optimality results for such a resource allocation problem and such a combination of multiple RMABPs.

Ketersediaan

Informasi Detail

Judul Seri

OPERATIONS RESEARCH; Vol.70 No.1 January-February 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 416-431

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MARKOV DECISION PROCESS
RESOURCE SHARING
RESTLESS BANDITS

Info Detail Spesifik

https://doi.org/10.1287/opre.2020.2066

Pernyataan Tanggungjawab

Jing Fu, Bill Moran, Peter G. Taylor

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