Simulation-based optimization of process control policies for inventory management in supply chains

Jay D. Schwartz, Wenlin Wang, Daniel Rivera

Research output: Contribution to journalArticle

109 Scopus citations

Abstract

A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain.

Original languageEnglish (US)
Pages (from-to)1311-1320
Number of pages10
JournalAutomatica
Volume42
Issue number8
DOIs
StatePublished - Aug 1 2006

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Keywords

  • Internal model control
  • Inventory control
  • Model predictive control
  • Simulation-based optimization
  • Supply chain management

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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