A Regression based Unconstraining Demand Method in Revenue Management

Unconstraining Methods for Revenue Management Systems under Small Demand

A Comparison of Unconstraining Methods to Improve Revenue Management Systems

Carrie Crystal Queenan • Mark Ferguson • Jon Higbie • Rohit Kapoor Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556-5646, USA College of Management, Georgia Institute of Technology, 800 West Peachtree Street, Atlanta, Georgia 30332-0520, USA Revenue Analytics, Inc., 100 Galleria Parkway, Suite 1500, Atlanta, Georgia 30339, USA Capital One, 1680 Capital One Drive,...

Unconstraining Methods in Revenue Management Systems: Research Overview and Prospects

Demand unconstraining is one of the key techniques to the success of revenue management systems. This paper surveys the history of research on unconstraining methods and reviews over 130 references including the latest research works in the area. We discuss the relationship between censored data unconstraining and forecasting and review five alternative unconstraining approaches. Thesemethods c...

Forecast combination in revenue management demand forecasting

The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined forecast quality. Three types of diversification are used: (a) diversification concerning the level of le...

A Genetic Algorithm for Choice-Based Network Revenue Management

In recent years, enriching traditional revenue management models by considering the customer choice behavior has been a main challenge for researchers. The terminology for the airline application is used as representative of the problem. A popular and an efficient model considering these behaviors is choice-based deterministic linear programming (CDLP). This model assumes that each customer bel...