Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Approximation algorithms for restless bandit problems
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Computing Time-Dependent Bid Prices in Network Revenue Management Problems
Transportation Science
Approximate Dynamic Programming for Ambulance Redeployment
INFORMS Journal on Computing
A systematic framework for dynamically optimizing multi-user wireless video transmission
IEEE Journal on Selected Areas in Communications
Wireless network virtualization as a sequential auction game
INFOCOM'10 Proceedings of the 29th conference on Information communications
Information Relaxations and Duality in Stochastic Dynamic Programs
Operations Research
Approximation algorithms for restless bandit problems
Journal of the ACM (JACM)
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Manufacturing & Service Operations Management
Multiechelon Procurement and Distribution Policies for Traded Commodities
Management Science
Lagrangian relaxation and constraint generation for allocation and advanced scheduling
Computers and Operations Research
Optimal index rules for single resource allocation to stochastic dynamic competitors
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
A Lagrangian approach to dynamic resource allocation
Proceedings of the Winter Simulation Conference
Dynamic Capacity Allocation to Customers Who Remember Past Service
Management Science
Stochastic game for wireless network virtualization
IEEE/ACM Transactions on Networking (TON)
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We consider a broad class of stochastic dynamic programming problems that are amenable to relaxation via decomposition. These problems comprise multiple subproblems that are independent of each other except for a collection of coupling constraints on the action space. We fit an additively separable value function approximation using two techniques, namely, Lagrangian relaxation and the linear programming (LP) approach to approximate dynamic programming. We prove various results comparing the relaxations to each other and to the optimal problem value. We also provide a column generation algorithm for solving the LP-based relaxation to any desired optimality tolerance, and we report on numerical experiments on bandit-like problems. Our results provide insight into the complexity versus quality trade-off when choosing which of these relaxations to implement.