Constant Regret Resolving Heuristics for Price-Based Revenue Management

• 成果类型: Article
• 署名作者: Wang, Yining; Wang, He
• 署名单位: State University System of Florida; University of Florida; University System of Georgia; Georgia Institute of Technology
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2021.2219
• 发表日期: 2022
• 页码: 3538-3557
• 关键词: resolving; self-adjusting controls; price-based revenue management; Dynamic pricing

摘要

Price-based revenue management is an important problem in operations management with many practical applications. The problemconsiders a sellerwho sells one ormultiple products over T consecutive periods and is subject to constraints on the initial inventory levels of resources. Whereas, in theory, the optimal pricing policy could be obtained via dynamic programming, computing the exact dynamic programming solution is often intractable. Approximate policies, such as the resolving heuristics, are often applied as computationally tractable alternatives. In this paper, we show the following two results for price-based network revenuemanagement under a continuous price set. First, we prove that a natural resolving heuristic attains O(1) regret compared with the value of the optimal policy. This improves the O(lnT) regret upper bound established in the prior work by Jasin in 2014. Second, we prove that there is anO(lnT) gap between the value of the optimal policy and that of the fluidmodel. This complements our upper bound result by showing that the fluid is not an adequate information-relaxed benchmarkwhen analyzing price-based revenuemanagement algorithms.