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