Dynamic Pricing of Information Products Based on Reinforcement Learning: A Yield-Management Approach
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Seat allocation for massive events based on region growing techniques
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
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
Capacity Rationing in Stochastic Rental Systems with Advance Demand Information
Operations Research
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
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Manufacturing & Service Operations Management
Network Cargo Capacity Management
Operations Research
Network capacity management under competition
Computational Optimization and Applications
Yield management of workforce for IT service providers
Decision Support Systems
Cargo Capacity Management with Allotments and Spot Market Demand
Operations Research
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
Simulation-based methods for booking control in network revenue management
Proceedings of the Winter Simulation Conference
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
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We investigate dynamic policies for allocating scarce inventory to stochastic demand for multiple fare classes, in a network environment so as to maximize total expected revenues. Typical applications include sequential reservations for an airline network, hotel, or car rental service. We propose and analyze a new algorithm based on approximate dynamic programming, both theoretically and computationally. This algorithm uses adaptive, nonadditive bid prices from a linear programming relaxation. We provide computational results that give insight into the performance of the new algorithm and the widely used bid-price control, for several networks and demand scenarios. We extend the proposed algorithm to handle cancellations and no-shows by incorporating oversales decisions in the underlying linear programming formulation. We report encouraging computational results that show that the new algorithm leads to higher revenues and more robust performance than bid-price control.