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Approximate dynamic programming algorithms for multidimensional inventory optimization problems

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date2013
Host publicationApproximate Dynamic Programming Algorithms for Multidimensional Inventory Optimization Problems Manufacturing Modelling, Management, and Control
PublisherIFAC
Pages2015-2020
Number of pages6
Original languageEnglish

Conference

Conference7th IFAC Conference on Manufacturing Modelling, Management and Control
CountryRussian Federation
CitySt. Petersburg
Period19/06/1321/06/13

Conference

Conference7th IFAC Conference on Manufacturing Modelling, Management and Control
CountryRussian Federation
CitySt. Petersburg
Period19/06/1321/06/13

Abstract

An important issue in the supply chain literature concerns the optimization of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions. However, as supply chain systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimizing single-product inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such complex application areas. Interrelated products and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be obtained by classical methods. We propose the methodology of Approximate Dynamic Programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup approach, ADP develops policies by utilizing only a fraction of the computations required by classical Dynamic Programming. However, there are no studies in the literature that optimize production decisions in a stochastic, multifactory, multiproduct inventory system of this complexity. This paper aims to explore the feasibility of ADP algorithms for this application. We present the results from a series of numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies.