Developing effective service policies for multiclass queues with abandonment: asymptotic optimality and approximate policy improvement
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Authors
James, Terry
Glazebrook, Kevin
Lin, Kyle
Subjects
Multiclass queue
Customer abandonment
Markov decision process
Index policy
Approximate policy improvement
Customer abandonment
Markov decision process
Index policy
Approximate policy improvement
Advisors
Date of Issue
2016
Date
Publisher
INFORMS
Language
Abstract
We study a single server queuing model with multiple classes and impatient customers. The goal is to determine a service policy to maximize the long-run reward rate earned from serving customers net of holding costs and penalties respectively due to customers waiting for and leaving before receiving service. We first show that it is without loss of generality to study a pure-reward model. Since standard methods can usually only compute the optimal policy for problems with up to three customer classes, our focus is to develop a suite of heuristic approaches, with a preference for operationally simple policies with good reward characteristics. One such heuristic is the RµΘ rule - a priority policy that ranks all customer classes based on the product of reward R, service rate µ, and abandonment rate Θ. We show that the RµΘ rule is asymptotically optimal as customer abandonment rates approach zero and often performs well in cases where the simpler Rµ rule performs poorly. The paper also develops an approximate policy improvement method that uses simulation and interpolation to estimate the bias function for use in a dynamic programming recursion. For systems with two or three customer classes, our numerical study indicates that the best of our simple priority policies is near optimal in most cases; when it is not, the approximate policy improvement method invariably tightens up the gap substantially. For systems with five customer classes, our heuristics typically achieve within 4% of an upper bound for the optimal value, which is computed via a linear program that relies on a relaxation of the original system. The computational requirement of the approximate policy improvement method grows rapidly when the number of customer classes or the traffic intensity increases.
Type
Article
Description
The article of record as published may be found at http://dx.doi.org/10. 1287/tjoc.2015.0675
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Department
Operations Research (OR)
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Format
14 p.
Citation
T. James, K. Glazebrook, K. Lin, "Developing effective service policies for multiclass queues with abandonment: asymptotic optimality and approximate policy improvement," INFORMS Journals on Computing, v.28, no.2 (Spring 2016), pp. 251-264.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.