engleski

Evaluation of Unconstraining Methods on Airlines' Revenue Management Systems

Airline revenue management systems are used to calculate the booking limits on each fare class to maximize expected revenue for all future flight departures. Its performance depends critically on the forecasting module that uses historical data to project future quantities of demand. Those data are censored or constrained by the imposed booking limits and do not represent true demand since rejected requests are not recorded. Eight unconstraining methods that transform the censored data into more accurate estimates of actual historical demand ranging from naive methods such as discarding all censored observation, to complex, such as Expectation Maximization Algorithm and Projection Detruncation Algorithm, are analysed and their accuracy is compared. Those methods are evaluated and tested on simulated data sets generated by ICE V2.0 software: first, data sets that represent true demand were produced, then the aircraft capacity was reduced and EMSRb booking limits for every booking class were calculated. These limits constrained the original demand data at various points of booking process and corresponding censored data sets were obtained. The unconstrained methods were applied to the censored observations and resulting unconstrained data were compared to the actual demand data and their performance was evaluated.

airline revenue management, demand forecasting, data censoring, unconstraining methods, Expectation Maximization Algorithm, Projection Detruncation Algorithm

Rad je prezentirala doc. dr. sc. Ružica Škurla Babić