A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty

Wenhe Ye, Fengqi You*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Simulation-based optimization can significantly improve operational efficiency of a supply chain network under uncertainty. However, both the noisiness and complexity render the simulation as a black-box function. We propose a novel regional surrogate based framework for inventory optimization in general supply chain networks under demand uncertainty. Both the objective value and service level constraints are estimated by the kriging method using regional information. The aggregated surrogate models are optimized by a trust-region framework. For a case study with 15 inventory storing nodes, the proposed algorithm returns an optimal solution in 2,994 seconds with 6,721 functional evaluations while the genetic algorithm (GA) returns a 36.2% higher objective value after 46,000 function evaluations.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2001-2006
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jan 1 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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