Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory de...

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Bibliographic Details
Published in:International journal of production research Vol. 55; no. 7; pp. 2034 - 2050
Main Authors: Çimen, Mustafa, Kirkbride, Chris
Format: Journal Article
Language:English
Published: London Taylor & Francis 03.04.2017
Taylor & Francis LLC
Subjects:
Algorithms
approximate dynamic programming
Decisions
Dynamic programming
flexible manufacturing
Inventories
Inventory control
Inventory management
machine learning
Manufacturing
Optimization
Policies
process flexibility
Stockpiling
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimising single inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such a complex application area. Decisions involving the interrelated product inventories and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be readily obtained. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup simulation approach, ADP develops policies by utilising only a fraction of the computations required by classical dynamic programming. However, there are few studies in the literature that optimise production decisions in a stochastic, multi-factory, multi-product inventory system of this complexity. This paper aims to explore the feasibility and relevancy of ADP algorithms for this application. We present the results from numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies and to policies derived from a deterministic approximation of the problem.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2016.1264643