Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Approximate dynamic programming algorithms for multidimensional inventory optimization problems
AU - Cimen, Mustafa
AU - Kirkbride, Christopher
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
U2 - 10.3182/20130619-3-RU-3018.00441
DO - 10.3182/20130619-3-RU-3018.00441
M3 - Conference contribution/Paper
SP - 2015
EP - 2020
BT - Approximate Dynamic Programming Algorithms for Multidimensional Inventory Optimization Problems Manufacturing Modelling, Management, and Control
PB - IFAC
T2 - 7th IFAC Conference on Manufacturing Modelling, Management and Control
Y2 - 19 June 2013 through 21 June 2013
ER -