Graduation Year

2004

Document Type

Thesis

Degree

M.S.I.E.

Degree Granting Department

Industrial Engineering

Major Professor

Suresh Khator, Ph.D.

Committee Member

Qiang Huang, Ph.D.

Committee Member

Kaushal Chari, Ph.D

Keywords

network, simulation, yield, ticket, pricing

Abstract

Ever since the deregulation of the airline industry in 1978, fierce competition has made every airline try and gain a competitive edge in the market. In order to accomplish this, airlines are turning to advanced optimization techniques such as revenue management. Revenue management is a way for airlines to maximize capacity and profitability by managing supply and demand through price management.

Over the last few years research in the field of revenue management has steadily progressed from seat inventory control techniques such as single leg seat inventory and network inventory control to ticket pricing techniques. Ticket pricing techniques involve setting ticket prices according to the time remaining to depart and inventory level conditions at that point in time. These models can be solved either by dynamic or mathematical programming. However, these models in addition to having increased complexity are based on several assumptions which may not be valid in real life situations thereby limiting there applicability.

In this research, we have developed computer simulation models using Arena software as a tool to solve airline revenue management problems. Different models based on factors such as customer behavior, which would involve the probability of a customer accepting a ticket and relevant pricing methods such as seats remaining and time remaining have been developed with the objective of reaching an optimal revenue management policy.

Initially, the strategies have been developed and tested for a single flight leg for different types of destinations such as tourist, business and mixed tourist and business. It was found that models where pricing was based on seats remaining generated the most revenue for the tourist destinations, time remaining for the business destinations and pricing based on time and seats remaining for the mixed type. Two different strategies, one where the ticket price for the indirect (stop-over) flight increases as more seats for direct flight are sold and the second where the ticket price for the indirect flight decreases have been developed for a network of three cities with direct and stop-over flights. It was found that the first strategy works well for the business destination. There was no significant difference between the two strategies for the other two destinations. Also, the model was run where a set percentage of seats on the direct flight are sold prior to the opening of indirect flight bookings (blocking). It was found that blocking of seats did not increase the total revenue generated.

Download

Share

COinS