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OPTIMAL ADMISSION AND ROUTING WITH CONGESTION-SENSITIVE CUSTOMER CLASSES

Published online by Cambridge University Press:  01 March 2021

Ayse Aslan Open the ORCID record for Ayse Aslan [Opens in a new window]
Ayse Aslan*
Affiliation:
Department of Operations, University of Groningen, Groningen 9747 AD, The Netherlands E-mail: ayse.aslan@rug.nl
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Abstract

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This paper considers optimal admission and routing control in multi-class service systems in which customers can either receive quality regular service which is subject to congestion or can receive congestion-free but less desirable service at an alternative service station, which we call the self-service station. We formulate the problem within the Markov decision process framework and focus on characterizing the structure of dynamic optimal policies which maximize the expected long-run rewards. For this, value function and sample path arguments are used. The congestion sensitivity of customers is modeled with class-independent holding costs at the regular service station. The results show how the admission rewards of customer classes affect their priorities at the regular and self-service stations. We explore that the priority for regular service may not only depend on regular service admission rewards of classes but also on the difference between regular and self-service admission rewards. We show that optimal policies have monotonicity properties, regarding the optimal decisions of individual customer classes such that they divide the state space into three connected regions per class.

Keywords

admission controlcongestionMarkov decision processesrevenue managementrouting control
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 36 , Issue 3 , July 2022 , pp. 774 - 798
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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