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

Research output: Contribution to journalArticle

Abstract

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.
Original languageEnglish
Pages (from-to)165-177
Number of pages13
JournalTransportation Research Procedia
Volume43
Early online date10 Jan 2020
DOIs
Publication statusE-pub ahead of print - 10 Jan 2020

Fingerprint

optimization model
flight
Genetic algorithms
Linear programming
revenue
demand
Seats
programming
Fuzzy logic
Profitability
logic
loyalty
reputation
penalty
profit
customer
uncertainty
Industry
cause
industry

Keywords

  • revenue management
  • overbooking
  • airline networks
  • fuzzy demand
  • genatic algorithm

Cite this

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights. / Al Bazi, Ammar; Uney, Emre; Abu Monshar, Anees.

In: Transportation Research Procedia, Vol. 43, 10.01.2020, p. 165-177.

Research output: Contribution to journalArticle

@article{c645628c2e21411badfe662d13c686de,
title = "Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights",
abstract = "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.",
keywords = "revenue management, overbooking, airline networks, fuzzy demand, genatic algorithm",
author = "{Al Bazi}, Ammar and Emre Uney and {Abu Monshar}, Anees",
year = "2020",
month = "1",
day = "10",
doi = "10.1016/j.trpro.2019.12.031",
language = "English",
volume = "43",
pages = "165--177",
journal = "Transportation Research Procedia",
issn = "2352-1457",
publisher = "Elsevier",

}

TY - JOUR

T1 - Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

AU - Al Bazi, Ammar

AU - Uney, Emre

AU - Abu Monshar, Anees

PY - 2020/1/10

Y1 - 2020/1/10

N2 - 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.

AB - 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.

KW - revenue management

KW - overbooking

KW - airline networks

KW - fuzzy demand

KW - genatic algorithm

U2 - 10.1016/j.trpro.2019.12.031

DO - 10.1016/j.trpro.2019.12.031

M3 - Article

VL - 43

SP - 165

EP - 177

JO - Transportation Research Procedia

JF - Transportation Research Procedia

SN - 2352-1457

ER -