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

Wenhe Ye; Fengqi You

doi:10.1109/ACC.2015.7171027

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

Chemical and Biological Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2001-2006
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7171027
State	Published - Jul 28 2015
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Other

Other	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	7/1/15 → 7/3/15

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2015.7171027

Cite this

Ye, W., & You, F. (2015). A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty. In ACC 2015 - 2015 American Control Conference (pp. 2001-2006). Article 7171027 (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7171027

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

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.",

author = "Wenhe Ye and Fengqi You",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

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doi = "10.1109/ACC.2015.7171027",

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Ye, W & You, F 2015, A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty. in ACC 2015 - 2015 American Control Conference., 7171027, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 2001-2006, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7171027

A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty. / Ye, Wenhe; You, Fengqi.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2001-2006 7171027 (Proceedings of the American Control Conference; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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

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