Revenue Management and Pricing Analytics (eBook)
XIX, 336 Seiten
Springer New York (Verlag)
978-1-4939-9606-3 (ISBN)
'There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.' Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft.
'The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it.' Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. 'This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.' Robert L. Phillips, Amazon'This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.' Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
'At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.' Richard Ratliff
Guillermo Gallego is the Department Head of Industrial Engineering and Decision Analytics, and also the Crown Worldwide Professor of Engineering.
"e;There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available."e; Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft."e;The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it."e; Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. "e;This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field."e; Robert L. Phillips, Director of Pricing Research at Amazon"e;At last, a serious and comprehensive treatment of modern revenue management andassortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference."e; Richard Ratliff, Research Scientist at Sabre "e;This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers."e; Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
Foreword 7
Preface 9
Acknowledgments 13
Contents 15
Part I Traditional Revenue Management 20
1 Single Resource Revenue Management with IndependentDemands 21
1.1 Introduction 21
1.2 Two Fare Classes 22
1.2.1 Continuous Demand Distributions 24
1.2.2 Quality of Service, Salvage Values, and Callable Products 25
1.3 Multiple Fare Classes 27
1.3.1 Structure of the Optimal Policy 28
1.3.2 Nonmonotone Fares 30
1.4 The Generalized Newsvendor Problem 31
1.5 Heuristics for Multiple Fare Classes 33
1.6 Bounds on Optimal Expected Revenue 37
1.6.1 Revenue Opportunity Model 40
1.7 General Fare Arrival Patterns with Poisson Demands 41
1.7.1 Model 42
1.7.2 Optimal Policy and Structural Results 43
1.7.3 Discrete-Time Formulation 44
1.8 Monotonic Fare Offerings 45
1.9 Compound Poisson Demands 48
1.10 Sequential vs. Mixed Arrival Formulations 50
1.11 End of Chapter Problems 51
1.12 Bibliographic Remarks 55
Appendix 55
Appendix 57
2 Network Revenue Management with Independent Demands 65
2.1 Introduction 65
2.2 Formulations 66
2.2.1 Upgrades and Upsells 70
2.2.2 Compound Poisson Process 71
2.2.3 Doubly Stochastic Poisson Process 71
2.3 Linear Programming-Based Upper Bound on V(T,c) 72
2.4 Bid-Prices and Probabilistic Admission Control 74
2.5 Refinements of Heuristics 77
2.5.1 Resolving the Deterministic Linear Program 77
2.5.2 Randomized Linear Program 77
2.5.3 Time-Dependent Bid-Prices 78
2.6 Dynamic Programming Decomposition 80
2.6.1 Exploiting the Deterministic Linear Program 81
2.6.2 Lagrangian Relaxation 83
2.7 Heuristics That Take Randomness into Account 87
2.7.1 Managing Itineraries 87
2.7.2 Managing Resources 88
2.8 Approximate Dynamic Programming 89
2.9 End of Chapter Problems 91
2.10 Bibliographical Remarks 94
Appendix 94
Appendix 96
3 Overbooking 100
3.1 Introduction 100
3.2 Overbooking for a Single Fare Class 101
3.3 Overbooking for Multiple Fare Classes 102
3.3.1 Optimal Booking Limits 104
3.3.2 Class-Dependent No-Show Refunds 105
3.3.3 Incorporating Cancellations 105
3.4 Overbooking over a Flight Network 106
3.4.1 Linear Programming-Based Upper Bound on V(T,0) 107
3.4.2 Book-and-Bump Strategy 109
3.4.3 Upper Bound for High Overbooking Penalties 109
3.4.4 Heuristics Based on the Linear Program 110
3.4.5 Other Approximation Strategies 111
3.5 End of Chapter Problems 112
3.6 Bibliographical Notes 115
Appendix 115
Appendix 116
Part II Revenue Management Under Customer Choice 123
4 Introduction to Choice Modeling 124
4.1 Introduction 124
4.2 Discrete Choice Models 125
4.3 Maximum and Random Utility Models 126
4.4 Basic Attraction and Multinomial Logit Models 127
4.5 Generalized Attraction Model 128
4.5.1 Independence of Irrelevant Alternatives 130
4.6 Nested Logit Model 131
4.7 Mixtures of Basic Attraction Models 133
4.8 The Exponomial Model 133
4.9 Random Consideration Set Model 134
4.10 Markov Chain Choice Model 135
4.11 Bounds and Approximate Choice Probabilities 138
4.12 Choice Models and Retailing 140
4.13 End of Chapter Questions 141
4.14 Bibliographic Remarks 142
5 Assortment Optimization 144
5.1 Introduction 144
5.2 The Assortment Optimization Problem 145
5.3 Maximum Utility Model 146
5.4 Independent Demand Model 147
5.5 Basic Attraction Model 147
5.6 Generalized Attraction Model 148
5.7 Mixtures of Basic Attraction Models 149
5.8 Nested Logit Model 150
5.9 Random Consideration Set Model 154
5.10 Markov Chain Choice Model 155
5.11 Constrained Assortment Optimization 157
5.11.1 Basic Attraction Model 157
Applications 159
5.11.2 Nested Logit Model 160
5.12 Convexity and Efficient Sets 163
5.13 End of Chapter Problems 166
5.14 Bibliographic Remarks 168
Appendix 168
Appendix 171
6 Single Resource Revenue Management with Dependent Demands 176
6.1 Introduction 176
6.2 Explicit Time Models 177
6.2.1 Formulation as an Independent Demand Model 179
6.2.2 Upper Bound and Bid-Price Heuristic 180
6.2.3 Monotone Fares 183
6.3 Implicit Time Models 185
6.3.1 Two Fare Classes 185
6.3.2 Heuristic Protection Levels 187
6.3.3 Theft Versus Standard Nesting and Arrival Patterns 189
6.3.4 Multiple Fare Classes 190
6.4 End of Chapter Problems 192
6.5 Bibliographical Remarks 194
Appendix 194
Appendix 194
7 Network Revenue Management with Dependent Demands 196
7.1 Introduction 196
7.2 Formulations 197
7.3 Linear Programming-Based Upper Bound on V(T,c) 199
7.3.1 Column Generation Procedure 200
7.3.2 Sales-Based Linear Program 201
Basic Attraction Model 201
Markov Chain Choice Model 203
7.3.3 Heuristics Based on the Linear Program 204
7.4 Dynamic Programming Decomposition 205
7.4.1 Exploiting the Deterministic Linear Program 205
7.4.2 Decomposition by Fare Allocation 207
7.4.3 Overbooking 210
7.5 End of Chapter Problems 211
7.6 Bibliographical Remarks 214
Appendix 214
Appendix 215
Part III Pricing Analytics 220
8 Basic Pricing Theory 221
8.1 Introduction 221
8.2 The Firm's Problem 222
8.2.1 Random Costs 223
8.3 The Representative Consumer's Problem 224
8.4 Finite Capacity 226
8.4.1 Lagrangian Relaxation 227
8.4.2 Finite Capacity and Finite Sales Horizon 228
8.5 Single Product Pricing Problems 229
8.5.1 Existence and Uniqueness 229
8.5.2 Priority Pricing 231
8.5.3 Social Planning and Dead Weight Loss 232
Call Options on Capacity 233
Bargaining Power 234
8.5.4 Multiple Market Segments 236
8.5.5 Peak Load Pricing 241
8.6 Multi-Product Pricing Problems 242
8.6.1 Linear Demand Model 244
8.6.2 The Multinomial Logit Model 245
8.6.3 The Nested Logit Model 247
8.7 End of Chapter Problems 249
8.8 Bibliographical Remarks 251
Appendix 251
Appendix 253
9 Dynamic Pricing Over Finite Horizons 259
9.1 Introduction 259
9.2 Single Product Dynamic Pricing 260
9.2.1 Examples with Closed Form Solution 261
9.2.2 Structural Results 263
9.2.3 Factors Affecting Dynamic Pricing 263
9.2.4 Discrete Time Formulation and Numerical Solutions 264
9.3 Extensions of Basic Model 265
9.3.1 Inventory Replenishments 265
9.3.2 Holding Costs 265
9.3.3 Discounted Cash Flows 265
9.3.4 Multiple Market Segments 266
9.3.5 Dynamic Pricing when Customers Negotiate 266
9.3.6 Compound Poisson 268
9.3.7 Dynamic Nonlinear Pricing 268
9.3.8 Strategic Customers and Monotone Pricing Policies 270
9.4 Fixed Price Policies for Time Independent Demands 270
9.5 Bid-Price Heuristics 272
9.6 Asymptotic Optimality of the Bid-Price Heuristic 274
9.7 The Surplus Process 276
9.8 Multi-Product Dynamic Pricing Problems 277
9.9 End of Chapter Problems 278
9.10 Bibliographical Remarks 281
Appendix 281
Appendix 283
10 Online Learning 288
10.1 Introduction 288
10.2 Ample Inventory Model 288
10.2.1 Regret 289
10.2.2 Assumptions 290
10.2.3 Preliminary Concepts 291
10.3 Constrained Inventory Model 296
10.4 Bibliographical Remarks 301
11 Competitive Assortment and Price Optimization 303
11.1 Introduction 303
11.2 Competitive Assortment Optimization 304
11.2.1 Problem Formulation 304
11.2.2 Existence of Equilibrium 305
11.2.3 Properties of Equilibrium 307
11.3 Dynamic Pricing Under Competition 308
11.3.1 Problem Formulation 308
11.3.2 Equilibrium Results 310
11.3.3 Comparative Statics 312
11.3.4 Asymptotic Optimality for the Stochastic Case 313
11.4 End of Chapter Problems 314
11.5 Bibliographical Remarks 316
Appendix 317
References 322
Index 343
| Erscheint lt. Verlag | 14.8.2019 |
|---|---|
| Reihe/Serie | International Series in Operations Research & Management Science | International Series in Operations Research & Management Science |
| Zusatzinfo | XIX, 336 p. 8 illus., 5 illus. in color. |
| Sprache | englisch |
| Themenwelt | Technik ► Bauwesen |
| Wirtschaft ► Allgemeines / Lexika | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion | |
| Wirtschaft ► Volkswirtschaftslehre ► Makroökonomie | |
| Schlagworte | Dynamic Pricing • Inventory Control • Operations Management • Operations Research • Revenue Management • stochastic models • Supply Chain Management |
| ISBN-10 | 1-4939-9606-1 / 1493996061 |
| ISBN-13 | 978-1-4939-9606-3 / 9781493996063 |
| Haben Sie eine Frage zum Produkt? |
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