Modeling and Analysis of Revenue Management in Airline Alliances

159 Pages·2010·1.3 MB·English

Modeling and Analysis of
Revenue Management in Airline Alliances

by
Christopher Pember Wright

Submitted in Partial Fulfillment
of the
Requirements for the Degree
Doctor of Philosophy

Supervised by
Professor Harry Groenevelt
Professor Robert A. Shumsky

William E. Simon
Graduate School of Business Administration

University of Rochester
Rochester, NY

2010
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Dedication

I dedicate this thesis to Amy.
I love you.
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Curriculum Vitae

Christopher Wright was born in Rochester, NY on May 3, 1975. He attended
Cornell University and graduated with a Bachelor of Science in Mechanical
Engineering in 1999. He started at the Simon School in 2001, completing his Master
of Business Administration in 2003 and earning admittance to the Simon PhD
program. He received a Simon School doctoral fellowship from 2003 to 2007 and,
upon admission to candidacy, received a Master of Science in Management Science
Methods in 2006. He has pursued his research in Operations Management under
Professor Harry Groenevelt and former Simon Professor Robert Shumsky.
While completing his PhD work, he also served as an instructor at three
universities. From Summer 2006 to Spring 2009, he served as an adjunct professor
and then visiting lecturer in Decision Sciences for the Saunders College of Business
at the Rochester Institute of Technology. In the Fall of 2006, he served as a lecturer
in Operations Management for the Simon School. Finally, in the Fall of 2009, he
started as an Assistant Professor of Management for the College of Business
Administration at Niagara University.
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Acknowledgements

I would like to thank my advisors Professors Harry Groenevelt and Robert
Shumsky for their input and support over the years, stemming back to my time in the
MBA program. Without their help I may never have started, much less completed,
my thesis.
Thanks also to Professor Greg Dobson both for serving as my third committee
member and for steering me in the direction of creative, hands-on teaching.
Additionally, I appreciate the comments and questions provided by Professor John
Long, the chair of my thesis proposal, and Professor Ravi Mantena, the reader.
Lastly, I am indebted to the numerous researchers and industry professionals that
provided feedback on our work at numerous conferences and talks.
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Abstract

From the most basic code-share agreements to the largest international alliances,
airlines are using itineraries that bundle seats on their own flight legs with those of
other airlines as a way to generate more revenue. With this growing practice comes
an increasing need for researchers to look at how airlines can best manage the sharing
of revenue between the airlines involved. This dissertation represents a first step
toward meeting that need. In three chapters, we model and analyze the process of
revenue management in airline alliances.
In Chapter 2, we begin by creating a model for the revenue management decisions
faced by two partners in an alliance. We formulate a finite-horizon Markov game,
over which the two airlines make accept/deny decisions on requests for itineraries on
not only their own networks, but on their partners' networks, as well. The key
distinctions between our model and previous single network models are the presence
of gaming mechanics and the existence of both intraline itineraries – those on a single
airline's network – and interline itineraries – those on both partners' networks. This
model is used as the basis for the remainder of the dissertation and also can serve as
the framework for any future research in the area.
In the remainder of Chapter 2, we examine the game of complete information, in
which both partners know each other's demand and revenue forecasts, as well as their
inventory levels. Specifically, we consider three dynamic schemes that change the
revenue shares received by each airline over the horizon based on the state of the
system, and contrast them with existing static schemes with fixed revenue shares. We
determine the equilibrium behavior for each scheme and provide insight into when
each may perform well. We then show that, while well selected static schemes can
perform similarly well, the dynamic schemes are far less susceptible to changes in the
demand faced by the alliance.
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In Chapter 3, we acknowledge the limitations of the complete information
assumption due to legal, computational and competitive reasons, and focus on a game
of incomplete information, in which partners do not know each other's inventory
levels or forecasts. We only allow them to share bid prices, a commonly calculated
quantity in modern revenue management systems. Through the selection of particular
sharing scheme and a clever heuristic assumption, we are able to decouple the
alliance problem into two single network problems connect by the posted bid prices.
The assumption – that the partner's bid prices will remain constant over the horizon –
is loosely based upon a result in the literature that is supported by numerical
simulation. We show that the application of this heuristic leads to very favorable
results despite the limited information shared between the partners. In addition,
because it decouples perfectly into single network problems, any one of the numerous
existing approximation schemes for that problem can be employed. This is
particularly beneficial since many of the approximation methods are already being
utilized in practice, facilitating the adoption of our heuristic for handling interline
itineraries.
In Chapter 4, we take the constant bid price assumption one step further, looking
at its application in the single airline network problem. We describe some schemes
from the literature that are logically consistent with the schemes shown in Chapter 3.
We also provide a new approximation method that in sample problems is shown to
provide high performance (% of optimal revenue) and stable bid prices. This scheme
utilizes a simultaneous-perturbation stochastic approximation method in searching for
the optimal vector of bid-prices. After examining their relative performance in the
single-network environment, we point out the characteristic of these schemes that
may lend themselves to performing well when combined with the decoupling alliance
heuristics provided in Chapter 3. Simulations of these schemes suggest that these
characteristics can have greater influence on alliance revenues than the performance
of the schemes in single networks.
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Contents

1 Introduction .........................................................................................1
2 Dynamic Revenue Sharing Schemes in a Game of
Complete Information .........................................................................6
2.1 Motivation .......................................................................................................6
2.2 Literature Review............................................................................................8
2.3 General Alliance Model ................................................................................11
2.3.1 The Demand Process.........................................................................13
2.3.2 Assumptions About Arrival Process and ..........................................14
2.3.3 Assumptions About Information Sharing .........................................14
2.3.4 Assumptions About Revenue Sharing ..............................................16
2.4 Centralized Control .......................................................................................16
2.4.1 The Decision Process ........................................................................17
2.4.2 Optimal Policies ................................................................................18
2.5 Decentralized Control ...................................................................................19
2.5.1 The Decision Process ........................................................................20
2.5.2 Non-Optimality of Markovian Transfer-Pricing Schemes ................23
2.5.3 General Equilibrium Results .............................................................24
2.6 Static Proration..............................................................................................28
2.7 Dynamic Transfer Prices...............................................................................32
2.7.1 Bid Price Scheme ..............................................................................33
2.7.2 Bid-Price Proration Scheme ..............................................................35
2.7.3 Partner Price Scheme ........................................................................37
2.8 Benefits of Surplus Sharing ..........................................................................40
2.9 Revenue Allocation .......................................................................................42
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2.10 Computational Limitations ...........................................................................42
2.11 Numerical Examples .....................................................................................43
2.11.1 Relative Performance of Dynamic Schemes ..................................43
2.11.2 Comparing Static and Dynamic Schemes ......................................47
2.12 Summary and Further Research ....................................................................54
3 Decoupling the Alliance: A Constant Bid-Price Approach
to the Game of Incomplete Information ......................................... 56
3.1 Motivation .....................................................................................................56
3.2 Additional Literature .....................................................................................57
3.3 A Bid-Price Only Game ................................................................................58
3.4 Constant Bid Price Heuristic (CBP) .............................................................59
3.4.1 Decoupling the Alliance Under CBP ................................................60
3.4.2 Additive Bid Prices ...........................................................................64
3.5 Equilibrium Results ......................................................................................66
3.5.1 Counterexample to Uniqueness ........................................................66
3.5.2 Uniqueness in a Special Case ...........................................................68
3.6 Benefits and Drawbacks of CBP...................................................................69
3.7 Numerical Experiments ................................................................................72
3.8 Prorated CBP (PCBP) Heuristic ...................................................................77
3.8.1 Overview of PCBP Method .............................................................77
3.8.2 Advantages and Drawbacks of PCBP ..............................................79
3.8.3 Numerical Example ..........................................................................80
3.9 Summary and Further Research ....................................................................82
4 Constant Bid Prices for Network and Alliance Revenue
Management: Benefiting from the Consistent Use of the
CBP Assumption .............................................................................. 84
4.1 Motivation ....................................................................................................84
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4.2 Additional Literature .....................................................................................85
4.3 Single Airline Network Model......................................................................86
4.4 Models Consistent with CBP ........................................................................86
4.4.1 Displacement Adjusted Virtual Nesting (DAVN) ............................87
4.4.1.1 DAVN Method ....................................................................87
4.4.1.2 Implementing IDAVN within CBP .....................................88
4.4.1.3 Contrasting DAVN with CBP .............................................89
4.4.2 Single-Network Constant Bid Price (CBP1).....................................91
4.4.2.1 CBP1 Method ......................................................................92
4.4.2.2 Sample Paths and CBP1 Method .........................................93
4.4.2.3 Finding the Best Bid Prices with SPSA ..............................95
4.4.2.4 An Example of the Search Algorithm .................................99
4.4.2.5 Implementing IDAVN within CBP ...................................103
4.4.3 Sample-Path Based Derivatives (SDR/SDD) .................................104
4.4.3.1 Sample-Path Based Derivatives Method ...........................104
4.4.4 Benefits of Consistent CBP Assumptions ......................................107
4.5 Scheme Consistent with PCBP ...................................................................114
4.5.1 Interative Prorated Expected Marginal Seat Revenue (IEMSR) ....115
4.5.1.1 IEMSR Method .................................................................115
4.5.1.2 Implementing IEMSR within PCBP .................................116
4.4.4 CBP1 in PCBP ................................................................................117
4.6 Summary and Conclusions .........................................................................118
5 Concluding Remarks ..................................................................... 120
References ............................................................................................ 123
Appendices ........................................................................................... 128
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List of Tables

2.1 Data for counter-example to transfer pricing optimality ...........................23
2.2 Summary of transfer prices paid to operating airline c ..............................40
2.3 Data for example illustrating benefit of premiums ....................................41
4.1 Notes on selecting SPSA parameter values from Spall (1998)..................96
A2 Table of parameter for sample alliances ..................................................145