Blind Dynamic Resource Allocation in Closed Networks via Mirror Backpressure

• 成果类型: Article
• 署名作者: Kanoria, Yash; Qian, Pengyu
• 署名单位: Columbia University; Purdue University System; Purdue University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4934
• 发表日期: 2024
• 页码: 5445-5462
• 关键词: control of queueing networks; BackPressure; mirror descent; no-underflow constraint

摘要

We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the origin to the destination of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our poliK+ ffiffiffiffififfi cies lose at most O �Kppayoff per customer relative to the optimal policy that T + 1 eta K knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and eta is the demand process' average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab.