Dynamic Pricing for Reusable Resources: The Power of Two Prices

• 成果类型: Article; Early Access
• 署名作者: Balseiro, Santiago R.; Ma, Will; Zhang, Wenxin
• 署名单位: Columbia University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0475
• 发表日期: 2025
• 关键词: reusable resources; Dynamic pricing; Online algorithms; loss network; asymptotic analysis

摘要

Motivated by real-world applications, such as rental and cloud computing services, we investigate pricing for reusable resources. We consider a system where a single resource with a fixed number of identical copies serves customers with heterogeneous willingness to pay (WTP), and the usage duration distribution is general. Optimal dynamic policies are computationally intractable when usage durations are not memoryless, so the existing literature has focused on static pricing, which incurs a steady-state performance loss of O( c ffiffi root ) compared with optimality when supply and demand scale with c. We propose a class of dynamic stock-dependent policies that (1) are computationally tractable and (2) can attain a steady-state performance loss of o(root ffificf). We give parametric bounds based on the local shape of the reward function at the optimal fluid admission probability and show that the performance loss of stock-dependent policies can be as low as O((logc)2). We characterize the tight performance loss for stock-dependent policies and show that they can in fact be achieved by a simple two-price policy that sets a higher price when the stock is below some threshold and a lower price otherwise. We extend our results to settings with multiple resources and multiple customer classes. Finally, we demonstrate that this minimally dynamic class of two-price policies performs well numerically, even in nonasymptotic settings, suggesting that a little dynamicity can go a long way.