Airline seat allocation with multiple nested fare classes
Operations Research
Optimizing multinomial logit profit functions
Management Science
An Analysis of Bid-Price Controls for Network Revenue Management
Management Science
Neuro-Dynamic Programming
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Asymptotic Behavior of an Allocation Policy for Revenue Management
Operations Research
Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand
Management Science
Commissioned Paper: An Overview of Pricing Models for Revenue Management
Manufacturing & Service Operations Management
Revenue Management for Parallel Flights with Customer-Choice Behavior
Operations Research
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Manufacturing & Service Operations Management
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Nonlinear Optimization
On the Choice-Based Linear Programming Model for Network Revenue Management
Manufacturing & Service Operations Management
Dynamic Bid Prices in Revenue Management
Operations Research
Dynamic Pricing and Inventory Control of Substitute Products
Manufacturing & Service Operations Management
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Dynamic pricing for a network of resources over a finite selling horizon has received considerable attention in recent years, yet few papers provide effective computational approaches to solve the problem. We consider a resource decomposition approach to solve the problem and investigate the performance of the approach in a computational study. We compare the performance of the approach to static pricing and choice-based availability control. Our numerical results show that dynamic pricing policies from network resource decomposition can achieve significant revenue lift compared with choice-based availability control and static pricing, even when the latter is frequently resolved. As a by-product of our approach, network decomposition provides an upper bound in revenue, which is provably tighter than the well-known upper bound from a deterministic approximation.