A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

• 成果类型: Article
• 署名作者: Maglaras, Costis; Eren, Serkan
• 署名单位: Columbia University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12243
• 发表日期: 2015
• 页码: 438-450
• 关键词: revenue management; censored demand; uncensoring; maximum entropy distributions

摘要

We propose a tractable, data-driven demand estimation procedure based on the use of maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare class Littlewood problem in a setting where the firm has access to only potentially censored sales observations; this is also known as the repeated newsvendor problem. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the spiral down effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.