An Analysis of Bid-Price Controls for Network Revenue Management
Management Science
Multiperiod Airline Overbooking with a Single Fare Class
Operations Research
Continuous-Time Airline Overbooking with Time-Dependent Fares and Refunds
Transportation Science
Airline Yield Management with Overbooking, Cancellations, and No-Shows
Transportation Science
Revenue Management in a Dynamic Network Environment
Transportation Science
Overbooking with Substitutable Inventory Classes
Operations Research
On the Choice-Based Linear Programming Model for Network Revenue Management
Manufacturing & Service Operations Management
Dynamic Bid Prices in Revenue Management
Operations Research
A Column Generation Algorithm for Choice-Based Network Revenue Management
Operations Research
From latex specifications to parallel codes
The Journal of Supercomputing
Skeletal based programming for dynamic programming on MultiGPU systems
The Journal of Supercomputing
Dynamic load balancing on heterogeneous multi-GPU systems
Computers and Electrical Engineering
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In this paper, we develop a revenue management model to jointly make the capacity allocation and overbooking decisions over an airline network. Our approach begins with the dynamic programming formulation of the capacity allocation and overbooking problem and uses an approximation strategy to decompose the dynamic programming formulation by the flight legs. This decomposition idea opens up the possibility of obtaining approximate solutions by concentrating on one flight leg at a time, but the capacity allocation and overbooking problem that takes place over a single flight leg still turns out to be intractable. We use a state aggregation approach to obtain high-quality solutions to the single-leg problem. Overall, our model constructs separable approximations to the value functions, which can be used to make the capacity allocation and overbooking decisions for the whole airline network. Computational experiments indicate that our model performs significantly better than a variety of benchmark strategies from the literature.