Asymptotically Optimal Clearing Control of Backlogs in Multiclass Processing Systems

Author:

Yu Lun1ORCID,Iravani Seyed2ORCID,Perry Ohad3ORCID

Affiliation:

1. School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China;

2. Department of Industrial Engineering and Management Science, Northwestern University, Evanston, Illinois 60208;

3. Department of Operations Research and Engineering Management, Southern Methodist University, Dallas, Texas 75205

Abstract

Processing systems, such as make-to-order production or service systems, are often faced with backlogged demand, which results in a prolonged period in which the system is congested, although it has sufficient processing capacity to handle all newly arriving demand. In “Asymptotically Optimal Clearing Control of Backlogs in Multiclass Processing Systems,” Yu, Iravani, and Perry consider a processing system, modeled by a multiclass queueing model, that faces the problem of optimally clearing a large backlog from several classes of customers (or orders). For the special case of two classes, the authors prove that a static priority policy following a discounted cμ/θ rule is asymptotically optimal. When there are more than two classes of customers, the authors show that any admissible control that follows the best-effort rule becomes asymptotically optimal after a relatively short time. An extensive numerical study shows that these proposed policies are effective and provides guidance on when to choose among the policies in practice.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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