수입관리에서 회귀모형 기반 수요 복원 방법 = A Regression based Unconstraining Demand Method in Revenue Management

다국어 초록 (Multilingual Abstract)

Accurate demand forecasting is a crucial component in revenue management(RM). The booking data of departed flights is used to forecast the demand for future departing flights; however, some booking requests that were denied were omitted in the departed flights data. Denied booking requests can be interpreted as censored in statistics. Thus, unconstraining demand is an important issue to forecast the true demands of future flights. Several unconstraining methods have been introduced and a method based on expectation maximization is considered superior. In this study, we propose a new unconstraining method based on a regression model that can entertain such censored data. Through a simulation study, the performance of the proposed method was evaluated with two representative unconstraining methods widely used in RM.

참고문헌 (Reference)

1 Talluri, K. T, "The Theory and Practice of Revenue Management" Kluwer Academic Publishers 2005

2 Cross, R. C., "Revenue Management: Hard-Core Tactics for Market Domination" Cassel 1997

3 Dempster, A. P., "Maximum likelihood from incomplete data via the EM algorithm" 39 : 1-38, 1977

4 Zeni, R. H., "Improved Forecast Accuracy in Revenue Management by Unconstraining Demand Es-timates from Censored Data" Rutgers University 2001

5 Weatherford, L. R., "Better unconstraining of airline demand data in revenue manage-ment systems for improved forecast accuracy and greater revenues" 1 : 234-254, 2002

6 Belobaba, P. P., "Air Travel Demand and Airline Seat Inventory Management" MIT 1987