Hava yolu gelir yönetiminde yolcuların uçuşa gelme oranının tahmini için bir bulanık yaklaşım

Revenue Management logic had been firstly used in aviation and then hotel sectors has still had big place in airlines' strategies increasing their revenue. Airline companies enabling market segmentation via Pricing and Yield Management methodologies aim to enlarge the number of passengers reached and obtain the maximized revenue. Overselling which is one of the foremost points of Yield Management is a very important income tool for airlines whose product is perishable and meaningless after departure. The possible cost revealed because of no-show passengers is compensated by overbooked customers. Show-up rates of the passengers are investigated in this thesis via six factors; fare, number of direct flights between origin and destination points, difficulty of having visa from destination country, human development index of destination country, distance and temperature and a forecast model is offered. There are six-month data instead of one whole year and the data is comprised of eight destination points, fuzzy linear regression method which is better in uncertain conditions and human perception is opted rather than statistical linear regression. At the end of application, show-up rate of a passenger is calculated between a minimum and maximum value for each line with respect to its characteristic. The main aim of the forecasting model is to predict the show-up rate of the passengers and minimize the error of number of overbooked customers for a brand-new airline.