Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator

Woonghee Tim Huh (), Retsef Levi (), Paat Rusmevichientong () and James B. Orlin ()
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Woonghee Tim Huh: Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Retsef Levi: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Paat Rusmevichientong: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
James B. Orlin: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Operations Research, 2011, vol. 59, issue 4, 929-941

Abstract: Using the well-known product-limit form of the Kaplan-Meier estimator from statistics, we propose a new class of nonparametric adaptive data-driven policies for stochastic inventory control problems. We focus on the distribution-free newsvendor model with censored demands. The assumption is that the demand distribution is not known and there are only sales data available. We study the theoretical performance of the new policies and show that for discrete demand distributions they converge almost surely to the set of optimal solutions. Computational experiments suggest that the new policies converge for general demand distributions, not necessarily discrete, and demonstrate that they are significantly more robust than previously known policies. As a by-product of the theoretical analysis, we obtain new results on the asymptotic consistency of the Kaplan-Meier estimator for discrete random variables that extend existing work in statistics. To the best of our knowledge, this is the first application of the Kaplan-Meier estimator within an adaptive optimization algorithm, in particular, the first application to stochastic inventory control models. We believe that this work will lead to additional applications in other domains.

Keywords: inventory control; data-driven algorithms; statistics (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (41)

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