A Metastrategy for Large-Scale Resource Management Based on Informational Decomposition
INFORMS Journal on Computing
A Price-Directed Approach to Stochastic Inventory/Routing
Operations Research
The optimizing-simulator: merging simulation and optimization using approximate dynamic programming
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Approximate dynamic programming: lessons from the field
Proceedings of the 40th Conference on Winter Simulation
A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand
Mathematics of Operations Research
Mathematics of Operations Research
Robust Controls for Network Revenue Management
Manufacturing & Service Operations Management
Robust Approximation to Multiperiod Inventory Management
Operations Research
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Online algorithms for the newsvendor problem with and without censored demands
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
Fully Distribution-Free Profit Maximization: The Inventory Management Case
Mathematics of Operations Research
Approximate dynamic programming for an inventory problem: Empirical comparison
Computers and Industrial Engineering
The Effect of Robust Decisions on the Cost of Uncertainty in Military Airlift Operations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Online lot-sizing problems with ordering, holding and shortage costs
Operations Research Letters
An algorithm for approximating piecewise linear concave functions from sample gradients
Operations Research Letters
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We consider the problem of optimizing inventories for problems where the demand distribution is unknown, and where it does not necessarily follow a standard form such as the normal. We address problems where the process of deciding the inventory, and then realizing the demand, occurs repeatedly. The only information we use is the amount of inventory left over. Rather than attempting to estimate the demand distribution, we directly estimate the value function using a technique called the Concave, Adaptive Value Estimation (CAVE) algorithm. CAVE constructs a sequence of concave piecewise linear approximations using sample gradients of the recourse function at different points in the domain. Since it is a sampling-based method, CAVE does not require knowledge of the underlying sample distribution. The result is a nonlinear approximation that is more responsive than traditional linear stochastic quasi-gradient methods and more flexible than analytical techniques that require distribution information. In addition, we demonstrate near-optimal behavior of the CAVE approximation in experiments involving two different types of stochastic programs--the newsvendor stochastic inventory problem and two-stage distribution problems.