Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
A Dynamic Model for Airline Seat Allocation with Passenger Diversion and No-Shows
Transportation Science
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Revenue Management for Parallel Flights with Customer-Choice Behavior
Operations Research
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
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
OM Practice---Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization
Manufacturing & Service Operations Management
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Manufacturing & Service Operations Management
Computing Bid Prices for Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice
Mathematics of Operations Research
Model Predictive Control for Dynamic Resource Allocation
Mathematics of Operations Research
Assessing the Value of Dynamic Pricing in Network Revenue Management
INFORMS Journal on Computing
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We consider a network revenue management problem where customers choose among open fare products according to some prespecified choice model. Starting with a Markov decision process (MDP) formulation, we approximate the value function with an affine function of the state vector. We show that the resulting problem provides a tighter bound for the MDP value than the choice-based linear program. We develop a column generation algorithm to solve the problem for a multinomial logit choice model with disjoint consideration sets (MNLD). We also derive a bound as a by-product of a decomposition heuristic. Our numerical study shows the policies from our solution approach can significantly outperform heuristics from the choice-based linear program.