Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights


Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2022, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: revenue management,overbooking,airline networks,fuzzy demand,genatic algorithm
Publication ISSN: 2352-1457
Last Modified: 09 Apr 2024 07:28
Date Deposited: 15 Feb 2023 13:05
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Related URLs: https://www.sci ... 5988?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2020-01-10
Published Online Date: 2020-01-10
Accepted Date: 2019-12-20
Authors: Al-Bazi, Dr Ammar (ORCID Profile 0000-0002-5057-4171)
Uney, Emre
Abu-Monshar, Anees

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