Airline seat allocation with multiple nested fare classes
Operations Research
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
Finite Horizon Stochastic Knapsacks with Applications to Yield Management
Operations Research
Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Revenue Management in a Dynamic Network Environment
Transportation Science
Overbooking with Substitutable Inventory Classes
Operations Research
Convex Optimization
Nonlinear Optimization
Toward Robust Revenue Management: Competitive Analysis of Online Booking
Operations Research
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
A Dynamic Programming Decomposition Method for Making Overbooking Decisions Over an Airline Network
INFORMS Journal on Computing
The Design of Approximation Algorithms
The Design of Approximation Algorithms
Regret in Overbooking and Fare-Class Allocation for Single Leg
Manufacturing & Service Operations Management
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In this paper, we consider the joint overbooking and capacity control problem over a single flight leg with multiple fare classes. The objective is to maximize the net expected revenue, which is given by the difference between the expected revenue from the accepted requests and the expected penalty cost from the denied reservations. We study a class of open loop policies that accept the requests for each fare class with a fixed acceptance probability. In this case, the challenge becomes finding a set of acceptance probabilities that maximize the net expected revenue. We derive a simple expression that can be used to compute the optimal acceptance probabilities, despite the problem of finding the optimal acceptance probabilities being a high dimensional optimization problem. We show that the optimal acceptance probabilities randomize the acceptance decisions for at most one fare class, indicating that the randomized nature of our open loop policies is not a huge practical concern. We bound the performance loss of our open loop policies when compared with the optimal policy. Computational experiments demonstrate that open loop policies perform remarkably well, providing net expected revenues within two percent of the optimal on average.