An Analysis of Bid-Price Controls for Network Revenue Management
Management Science
A Randomized Linear Programming Method for Computing Network Bid Prices
Transportation Science
Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Revenue Management in a Dynamic Network Environment
Transportation Science
Revenue Management and E-Commerce
Management Science
Operations Research
Simulation-Based Booking Limits for Airline Revenue Management
Operations Research
Expected Value of Distribution Information for the Newsvendor Problem
Operations Research
Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach
Manufacturing & Service Operations Management
A Nonparametric Approach to Multiproduct Pricing
Operations Research
A Robust Optimization Approach to Inventory Theory
Operations Research
Revenue Management with Limited Demand Information
Management Science
Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models
Mathematics of Operations Research
Dynamic Bid Prices in Revenue Management
Operations Research
Regret in the Newsvendor Model with Partial Information
Operations Research
The Role of Robust Optimization in Single-Leg Airline Revenue Management
Management Science
A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand
Mathematics of Operations Research
Toward Robust Revenue Management: Competitive Analysis of Online Booking
Operations Research
Robust solutions of uncertain linear programs
Operations Research Letters
Markdown Pricing with Unknown Fraction of Strategic Customers
Manufacturing & Service Operations Management
Hi-index | 0.00 |
Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations for the capacity allocation problem in revenue management using the maximin and the minimax regret criteria under general polyhedral uncertainty sets. Our approach encompasses the following open-loop controls: partitioned booking limits, nested booking limits, displacement-adjusted virtual nesting, and fixed bid prices. In specific problem instances, we show that a booking policy of the type of displacement-adjusted virtual nesting is robust, both from maximin and minimax regret perspectives. Our numerical analysis reveals that the minimax regret controls perform very well on average, despite their worst-case focus, and outperform the traditional controls when demand is correlated or censored. In particular, on real large-scale problem sets, the minimax regret approach outperforms by up to 2% the traditional heuristics. The maximin controls are more conservative but have the merit of being associated with a minimum revenue guarantee. Our models are scalable to solve practical problems because they combine efficient (exact or heuristic) solution methods with very modest data requirements.