An Analysis of Bid-Price Controls for Network Revenue Management
Management Science
A Randomized Linear Programming Method for Computing Network Bid Prices
Transportation Science
Revenue Management in a Dynamic Network Environment
Transportation Science
Dynamic Bid Prices in Revenue Management
Operations Research
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Bid-Price Controls for Network Revenue Management: Martingale Characterization of Optimal Bid Prices
Mathematics of Operations Research
Robust Controls for Network Revenue Management
Manufacturing & Service Operations Management
Computing Time-Dependent Bid Prices in Network Revenue Management Problems
Transportation Science
Dynamic control mechanisms for revenue management with flexible products
Computers and Operations Research
A Dynamic Programming Decomposition Method for Making Overbooking Decisions Over an Airline Network
INFORMS Journal on Computing
Computing Bid Prices for Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
Lagrangian relaxation and constraint generation for allocation and advanced scheduling
Computers and Operations Research
Cargo Capacity Management with Allotments and Spot Market Demand
Operations Research
A Lagrangian approach to dynamic resource allocation
Proceedings of the Winter Simulation Conference
Assessing the Value of Dynamic Pricing in Network Revenue Management
INFORMS Journal on Computing
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We propose a new method to compute bid prices in network revenue management problems. The novel aspect of our method is that it explicitly considers the temporal dynamics of the arrivals of the itinerary requests and generates bid prices that depend on the remaining leg capacities. Our method is based on relaxing certain constraints that link the decisions for different flight legs by associating Lagrange multipliers with them. In this case, the network revenue management problem decomposes by the flight legs, and we can concentrate on one flight leg at a time. When compared with the so-called deterministic linear program, we show that our method provides a tighter upper bound on the optimal objective value of the network revenue management problem. Computational experiments indicate that the bid prices obtained by our method perform significantly better than the ones obtained by standard benchmark methods.