An Analysis of Bid-Price Controls for Network Revenue Management
Management Science
Neuro-Dynamic Programming
A Dynamic Model for Airline Seat Allocation with Passenger Diversion and No-Shows
Transportation Science
Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Revenue Management in a Dynamic Network Environment
Transportation Science
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Revenue Management of Flexible Products
Manufacturing & Service Operations Management
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
Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
An Analysis of the Control-Algorithm Re-solving Issue in Inventory and Revenue Management
Manufacturing & Service Operations Management
Dynamic Bid Prices in Revenue Management
Operations Research
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Dynamic Capacity Management with Substitution
Operations Research
A Column Generation Algorithm for Choice-Based Network Revenue Management
Operations Research
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We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can be viewed as a variant of the classical dynamic programming decomposition widely used in the research and practice of network RM. The computational cost of this new decomposition approach is only slightly higher than the classical version. A numerical study shows that heuristic control policies from the decomposition consistently outperform policies from the classical decomposition.