Multivariate statistical simulation
Multivariate statistical simulation
The Censored Newsvendor and the Optimal Acquisition of Information
Operations Research
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models
Mathematics of Operations Research
Manufacturing & Service Operations Management
Regret in the Newsvendor Model with Partial Information
Operations Research
Mathematics of Operations Research
Solving operational statistics via a Bayesian analysis
Operations Research Letters
A practical inventory control policy using operational statistics
Operations Research Letters
Proceedings of the Winter Simulation Conference
A simulation-based approach to statistical inventory management
Proceedings of the Winter Simulation Conference
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Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. In this paper, we consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives: (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.