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http://dx.doi.org/10.5351/KJAS.2015.28.3.467

A Regression based Unconstraining Demand Method in Revenue Management  

Lee, JaeJune (Department of Statistics, Inha University)
Lee, Woojoo (Department of Statistics, Inha University)
Kim, Junghwan (Department of Statistics, Inha University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.3, 2015 , pp. 467-475 More about this Journal
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.
Keywords
unconstraining; censored data; expectation maximization; revenue management;
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