A computationally efficient simulation-based optimization method with region-wise surrogate modeling for stochastic inventory management of supply chains with general network structures

Wenhe Ye, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Simulation-based optimization is widely used to improve the performance of an inventory system under uncertainty. However, the black-box function between the input and output, along with the expensive simulation to reproduce a real inventory system, introduces a huge challenge in optimizing these performances. We propose an efficient framework for reducing the total operation cost while satisfying the service level constraints. The performances of each inventory in the system are estimated by kriging models in a region-wise manner which greatly reduces the computational time during both sampling and optimization. The aggregated surrogate models are optimized by a trust-region framework where a model recalibration process is used to ensure the solution's validity. The proposed framework is able to solve general supply chain problems with the multi-sourcing capability, asynchronous ordering, uncertain demand and stochastic lead time. This framework is demonstrated by two case studies with up to 18 nodes with inventory holding capability in the network.

Original languageEnglish (US)
Pages (from-to)164-179
Number of pages16
JournalComputers and Chemical Engineering
Volume87
DOIs
StatePublished - Apr 6 2016

Keywords

  • Inventory management
  • Kriging
  • Simulation-based optimization
  • Surrogate modeling
  • Trust-region algorithm

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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