An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Manufacturing & Service Operations Management
A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice
Mathematics of Operations Research
Dynamic Pricing with Financial Milestones: Feedback-Form Policies
Management Science
Blind Network Revenue Management
Operations Research
Blind Network Revenue Management
Operations Research
Bid Prices When Demand Is a Mix of Individual and Batch Bookings
Transportation Science
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While inventory-and revenue-management problems can be represented as Markov decision process (MDP) models, in some cases the well-known dynamic-programming curse of dimensionality makes it computationally prohibitive to solve them exactly. An alternative solution, called here the control-algorithm approach, is to use a math program (MP) to approximately represent the MDP and use its optimal solution to heuristically instantiate the parameters of the decision rules of a given set of control policies. As new information is observed over time, the control algorithm can incorporate it by re-solving the MP and revising the parameters of the decision rules with the newly obtained solution. The re-solving issue arises when one reflects on the consequences of this revision: Does the performance of the control algorithm really improve by revising its decision-rule instantiation with the solution of the re-solved MP, or should an appropriate modification of the prior solution be used? This paper analyzes the control-algorithm re-solving issue for a class of finite-horizon inventory-and revenue-management problems. It establishes sufficient conditions under which re-solving does not deteriorate the performance of a control algorithm, and it applies these results to control algorithms for network revenue management and multiproduct make-to-order production with lost sales and positive lead time.