Near-Optimal Pricing and Resource Allocation in a Large-Scale Service System

• 成果类型: Article; Early Access
• 署名作者: Wu, Zerui; Liu, Ran; Sun, Xu
• 署名单位: Shanghai Jiao Tong University; University of Miami
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2024.1073
• 发表日期: 2025
• 关键词: revenue management; arrival

摘要

We study dynamic pricing and resource allocation in large-scale service systems where multiple service units serve customers who are both price and delay sensitive. Customers are segmented into classes characterized by class-specific service rates and demand functions shaped by posted prices and estimated delays. To jointly optimize revenue and delay performance, we propose a family of state-dependent greedy heuristics that (i) assign dedicated service capacities to each customer class, and (ii) dynamically set prices by solving a tractable one-step optimization problem. Despite their simplicity, these heuristics achieve a relative optimality gap of O(n-2=3log n) in large-market regimes where demand scales with the number of servers n. We further establish that replacing the dedicatedcapacity rule with any work-conserving allocation preserves the same order of optimality. Numerical experiments confirm the efficacy and robustness of our approach and offer additional insights, including the counterintuitive finding that congestion-based pricing can be nonmonotonic in system congestion when customers observe queue-based waittime estimates.