Browsing by Subject "Probabilistic graphical models"

Now showing 1 - 1 of 1

Open Access
Missed flight cover design
(2019-07) Çelik, Beyza
Missed flight cover is an option with a price and validity period and is a source of ancillary revenues for the airline companies and helps passengers, who missed their flights, resume their journeys at reduced costs. We study optimal price and validity period of this option to allow a passenger to use missed flight fare towards the purchase of a future airline ticket. Our objective is to maximize the expected ancillary revenues of the airline. The possible actions of passengers are described with a probabilistic graphical model. Within that model, passenger's decision to buy the option and to resume the journey after a missed flight are described with separate hierarchical Bayesian mixed logit regression models. To estimate the parameters of those mixed logit models, an individualized Bayesian choice-based conjoint experiment is designed. In this experiment, each choice set is optimally picked so as to maximize the expected Kullback-Leibler divergence between subsequent posterior distributions of individualized part-worths. The posterior distributions of unknown model parameters, particularly, individualized part-worths, are calculated with a hybrid Markov Chain Monte Carlo (MCMC) algorithm. We developed an R-Shiny online survey web application for six di erent individualized choice experiments (buy or not buy an option for leisure and business travel, resume or not resume a missed leisure or business flight with or without an option) and collected responses of over 300 individuals. Using the MCMC samples of individual part-worths from their posterior distributions, we simulated the market. We searched and found an option design that maximized the average net revenue of the airline over the simulated runs of the market.