Latest revision as of 13:45, 26 January 2021

Abstract

In this thesis, airline network revenue management problem is considered for the case with no cancellations and overbooking. In literature, there exist several approximate probabilistic and deterministic mathematical models developed in order to maximize expected revenue at the end of the reservation period. The aim of this study is to develop models considering also the risks involved in the proposed booking control policies. Two linear programming models are proposed which incorporate the variance of the revenue. The objective of the models is to effectively balance the tradeoff between the expectation and variance of the revenue. The performances of the proposed models are compared to the previous models through a numerical study. The seat allocations resulting from the mathematical models are used in a simulation model working with several booking control policies. The probability distributions of the revenues are investigated and the revenues are compared in terms of expectation, standard deviation, coefficient of variation and probability of poor performance. It is observed that the use of the proposed models decreases the variability of the revenue and thereby the risk of probability of poor performance. Also, the expected revenues obtained by implementing the solutions of the proposed models with nested booking control policies turn out to be higher than other probabilistic models as long as the degree of variance incorporation is within some interval. When compared with the deterministic models, the proposed models provides for the decision makers with alternative, preferable policies in terms of the expectation and the variability measures. M.S. - Master of Science

Original document

The different versions of the original document can be found in:

• https://hdl.handle.net/11511/17022