Decision Support System for Predicting the Number of No-Show Passengers in Airline Industry

Abstract

Airline decision about how many seats to allow to be overbooked is based on the expectation of the number of passengers who will not show up on a specific flight. This paper proposes a decision support system for predicting the number of no show passengers that combines the case-based reasoning (CBR) approach with Interpolative Boolean Algebra (IBA) and considers recommendations from both expert and algorithm. More precisely, recently proposed IBA similarity measure along with suitable aggregation operator is used for comparing alternatives in CBR algorithms. The proposed system was tested on the real-life data of the Belgrade-Amsterdam route. The obtained results show the necessity to include expert knowledge in the prediction process. Furthermore, the results are indicating that IBA-based models perform significantly better comparing to traditional distance-based models. The proposed expert system should contribute to an airline utilizing its inventory, which will further result in profit increase.