A comparison of unconstraining methods to improve revenue management systems

• 成果类型: Article
• 署名作者: Queenan, Carrie Crystal; Ferguson, Mark; Higbie, Jon; Kapoor, Rohit
• 署名单位: University of Notre Dame; University System of Georgia; Georgia Institute of Technology; Capital One Financial Corporation
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• 发表日期: 2007
• 页码: 729-746
• 关键词: revenue management; truncated demand; forecasting; unconstrained demand; simulation

摘要

A successful revenue management system requires accurate demand forecasts for each customer segment. The forecasts are used to set booking limits for lower value customers to ensure an adequate supply for higher value customers. The very use of booking limits, however, constrains the historical demand data needed for an accurate forecast. Ignoring this interaction leads to substantial penalties in a firm's potential revenues. We review existing unconstraining methods and propose a new method that includes some attractive properties not found in the existing methods. We evaluate several of the common unconstraining methods against our proposed method by testing them on intentionally constrained simulated data. Results indicate our proposed method outperforms other methods in two of three data sets. We also test the revenue impact of our proposed method, expectation maximization (EM), and no unconstraining on actual booking data from a hotel/casino. We show that performance varies with the initial starting protection limits and a lack of unconstraining leads to significant revenue losses.