Fare Adjustment Strategies for Airline Revenue Management and Reservation Systems JUN 0 7 ...

95 Pages·2014·16.29 MB·English

Fare Adjustment Strategies for
Airline Revenue Management and Reservation Systems
By
Yin Shiang Valenrina Soo
B.B.A. (Hons), NUS Business School
National University of Singapore, 2001
SUBMITTED TO THE DEPARTMENT OF CIVIL AND ENVIRONMENTAL
ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF
MASTER OF SCIENCE IN TRANSPORTATION
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2007
© 2007 Massachusetts Institute of Technology. All rights reserved.
Signature of Author: ................................................ 1 ......
Department of Civil and Environmental Enginering
May 10, 2007
Certified by: ..........................................................
Peter P. Belobaba
Principal Research Scientist of Aeronautics and Astronautics
Thesi u ervisor
C ertified by: ........................................................... .... , ...,..., ..........
Joseph 4At Sussman
JR East ssor of Civil and Environmental Engineering
Thesis Reader
Ac cepted by : ................................................ .................................................
Daniele Veneziano
Chairman, Departmental Committee for Graduate Students
MASSACHUSETTS INSTI E
OF TECHNOLOGY
JUN 0 7 2007 ARKER
LIBRARIES

Fare Adjustment Strategies for
Airline Revenue Management and Reservation Systems
by
Yin Shiang Valenrina Soo
Submitted to the Department of Civil and Environmental Engineering on May 10, 2007
in Partial Fulfillment of the Requirements for the Degree of Master of Science in Transportation
ABSTRACT
With the growth of Low Cost Carriers (LCC) and their use of simplified fare structures, the
airline industry has seen an increased removal of many fare restrictions, especially in markets
with intense LCC presence. This resulted in "semi-restricted" fare structure where there are
homogenous fare classes that are undifferentiated except by price and also distinct fare classes
which are still differentiated by booking restrictions and advance purchase requirements. In this
new fare environment, the use of traditional Revenue Management (RM) systems, which were
developed based on the assumption of independence of demand of fare classes, tend to lead to a
spiral down effect. Airlines now have to deal with customers who systematically buy the lowest
fare available in the absence of distinctions between the fare classes. This result in fewer
bookings observed in the higher fare classes, leading to lower forecast and less protection of seats
for the higher yield passengers.
This thesis describes Fare Adjustment, a technique developed for network RM systems, which
acts at the booking limit optimizer level as it takes into account the sell-up potential of
passengers (the probability that a passenger is willing to buy a higher-fare ticket if his request is
denied). The goal of this thesis is to provide a more comprehensive investigation into the
effectiveness of fare adjustment as a tool to improve airline revenues in this new environment by
1) extending the investigation of the effectiveness of fare adjustment with standard forecasting to
leg-based RM systems (namely EMSRb and HBP) and also a mixed fare structure where different
fare structures are used for different markets, and 2) looking at the alternative use of fare
adjustment in the reservation system.
Experiments with the Passenger Origin-Destination Simulator demonstrate that RM Fare
Adjustment with standard forecasting can improve an airline's network revenue by 0.8% to 1.3%
over standard revenue management methods. In particular, RM Fare Adjustment reduces the
aggressiveness of path forecasting through the lowering of bid prices as it takes into account the
risk of buying-down. Simulations of Fare Adjustment in the Reservation System also showed
positive results with revenue improvement of about 0.4% to 0.7%.
Thesis Supervisor: Dr. Peter P. Belobaba
Title: Principal Research Scientist of Aeronautics and Astronautics
Thesis Reader: Dr. Joseph M. Sussman
Title: JR East Professor of Civil and Environmental Engineering

Acknowledgements
First and foremost, many thanks to Dr. Peter Belobaba for being my academic and
research advisor. He introduced me to the world of revenue management and taught me
all that I needed to know. I appreciate his guidance, mentorship and the opportunities he
has given me during these past 2 years here in MIT.
I must also thank all the airlines in the PODS consortium for making this research
possible through their financial support and continuous feedback. Acknowledgement also
goes to our programmer, Craig Hopperstad, for helping me understand the workings of
the PODS simulator.
As for all my fellow colleagues (past and present) in the MIT International Center for Air
Transportation and my friends in the MST program, thank you for helping me get
through the years. Special thanks to Maital Dar, Thierry Vanhaverbeke and Greg Zerbib
for teaching me the ropes and helping me learn about PODS.
I must also thank all my friends from the MIT Ballroom Dance Team in particular Jing
Wang, Mingzhi Liu and Royson Chong. Without them, my life would have been a lot less
exciting and my drawers would not be filled with so many ribbons and awards.
To my family back home in Singapore, thank you for all that you have done for me. I
would not be who I am today without you guys. Thank you for your unwavering love,
care and concern for me.
To Escamillo, my beloved husband, thank you for your love, understanding and support.
You are always there for me, encouraging me and cheering me on. Now that I am ending
this "Graduate School in MIT" chapter of my life, I look forward to starting a whole new
"Life in NYC" chapter with you. You are the best husband and friend one can ever hope
for. I love you.
And last but not least, I thank God for His love for me. Thanks to Him for giving me the
strength, the courage and faith to carry on even when things sometimes look impossible.
He has been my stronghold and shelter and I thank Him for all the provisions He had
given me and the miracles He had performed in my life.
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-IMLE OF CONTENTS
Page
10
LIST OF FIGURES
13
LIST OF TABLES
1. CHAPTER ONE: INTRODUCTION 15
1.1 REVENUE MANAGEMENT 15
1.2 CHANGES IN THE INDUSTRY 17
1.3 NEW / RECENT APPROACHES 18
1.3.1 Forecasting and Sell-up 18
1.3.2 Q/Hybrid Forecasting 19
1.3.3 Fare Adjustment 19
1.4 OBJECTIVES OF THE THESIS 20
1.5 ORGANIZATION OF THE THESIS 21
2. CHAPTER TWO: LITERATURE REVIEW 23
2.1 REVENUE MANAGEMENT 23
25
2.1.1 Forecasting
2.1.2 Seat Inventory Control 26
2.2 LOW COST CARRIERS AND A LESS-RESTRICTED ENVIRONMENT 29
2.3 REVENUE MANAGEMENT TOOLS FOR THE NEW ENVIRONMENT 31
2.3.1 Q / Hybrid Forecasting 32
2.3.2 Fare Adjustment 33
2.4 CHAPTER SUMMARY 35
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3. CHAPTER THREE: SIMULATION APPROACH TO RM 37
3.1 THE PODS SIMULATION TOOL 37
3.1.1 Passenger Choice Model 39
3.1.2 PODS Revenue Management Systems 41
3.2 SIMULATION ENVIRONMENT 42
3.2.1 Network D 42
3.2.2 Network S 44
3.3 FARE ADJUSTMENT IN REVENUE MANAGEMENT SYSTEM 48
3.3.1 FRAT5s 49
3.3.2 Sell up 50
3.3.3 Formulation and Parameters in PODS 52
3.4 FARE ADJUSTMENT IN RESERVATION SYSTEM 56
3.5 CHAPTER SUMMARY 57
4. CHAPTER FOUR: FARE ADJUSTMENT IN RM SYSTEM 59
4.1 NETWORK D 59
4.1.1 DAVN 60
4.1.2 EMSRb with Path Forecasting 61
4.1.3 EMSRb (Path) with Fare Adjustment 61
4.1.4 HBP 65
4.2 NETWORK S 67
4.2.1 EMSRb with Path Forecasting 68
4.2.2 Path-based EMSRb with Fare Adjustment 69
4.2.3 HBP with Fare Adjustment 70
4.2.4 DAVN with Fare Adjustment 73
4.3 CHAPTER SUMMARY 75
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5. CHAPTER FIVE: FARE ADJUSTMENT IN RESERVATION SYSTEM 77
5.1 NETWORK D 77
5.1.1 Introduction of RES Bid Price to Leg-Based EMSRb 77
5.1.2 Leg-Based EMSRb with RES Fare Adjustment 79
5.1.3 Leg-Based HBP with RES Fare Adjustment 81
5.2 NETWORK S 83
5.2.1 Leg-Based EMSRb with RES Fare Adjustment 83
5.2.2 Leg-Based HBP with RES Fare Adjustment 87
5.3 CHAPTER SUMMARY 89
6. CHAPTER SIX: CONCLUSIONS 91
6.1 SUMMARY OF FINDINGS 92
6.2 FURTHER RESEARCH DIRECTIONS 94
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T OF FIGURES
Page
1. CHAPTER ONE: INTRODUCTION
Figure 1-1 Use of Differential Pricing to Maximize Revenue 16
2. CHAPTER TWO: LITERATURE REVIEW
Figure 2-1 Example of a Third Generation System 24
Figure 2-2 Spiral-Down Effect 31
Figure 2-3 Co-existence of Different Fare Structures in a Network 33
Figure 2-4 Using Fare Adjustment to Decouple the Fare Structures 34
3. CHAPTER THREE: SIMULATION APPROACH TO RM
Figure 3-1 PODS Structure 39
Figure 3-2 Network D 43
Figure 3-3 ALl's Network 43
Figure 3-4 AL2's Network 43
Figure 3-5 ALl's Route Network in Network S 45
Figure 3-6 AL2's Route Network in Network S 46
Figure 3-7 AL3's Route Network in Network S 46
Figure 3-8 AL4's Route Network in Network S 47
Figure 3-9 FRAT5 Curves in PODS 49
Figure 3-10 PODS FA FRAT5s Values with Different Scaling Factors 50
Figure 3-11 Probability of Sell-up and FRAT5s 51
Figure 3-12 Relationship between Fare, Marginal Revenue and PE Cost 53
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