TY - JOUR

T1 - Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

AU - Cimen, Mustafa

AU - Kirkbride, Christopher

N1 - 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

PY - 2017/4

Y1 - 2017/4

N2 - 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.

AB - 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.

KW - approximate dynamic programming

KW - machine learning

KW - dynamic programming

KW - flexible manufacturing

KW - process flexibility

KW - inventory control

U2 - 10.1080/00207543.2016.1264643

DO - 10.1080/00207543.2016.1264643

M3 - Journal article

VL - 55

SP - 2034

EP - 2050

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 7

ER -