Home > Research > Publications & Outputs > Approximate dynamic programming algorithms for ...

Associated organisational unit

Electronic data

  • IJPR_lookupInv_postprint

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 13/12/2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1264643

    Accepted author manuscript, 810 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>04/2017
<mark>Journal</mark>International Journal of Production Research
Issue number7
Volume55
Number of pages17
Pages (from-to)2034-2050
Publication statusPublished
Early online date13/12/16
Original languageEnglish

Abstract

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.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 13/12/2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1264643