4.7 Article

A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 304, 期 2, 页码 515-524

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ELSEVIER
DOI: 10.1016/j.ejor.2022.04.011

关键词

Inventory; Correlated demand; Stochastic programming; Mixed integer linear programming; Martingale model of forecast evolution

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This paper extends the single-item single-stocking location nonstationary stochastic inventory problem by relaxing the assumption of independent demand. It presents a mathematical programming-based solution method utilizing an existing piecewise linear approximation strategy. The method can handle various demand assumptions and demonstrates near-optimal plans compared to exact solutions obtained via stochastic dynamic programming.
This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solu-tion method built upon an existing piecewise linear approximation strategy under the receding horizon control framework. Our method can be implemented by leveraging off-the-shelf mixed-integer linear pro-gramming solvers. It can tackle demand under various assumptions: the multivariate normal distribution, a collection of time-series processes, and the Martingale Model of Forecast Evolution. We compare against exact solutions obtained via stochastic dynamic programming to demonstrate that our method leads to near-optimal plans.(c) 2022 Elsevier B.V. All rights reserved.

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