DocumentCode :
3351495
Title :
A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty
Author :
Wenhe Ye ; Fengqi You
Author_Institution :
Dept. of Chem. & Biol. Eng., Northwestern Univ., Evanston, IL, USA
fYear :
2015
fDate :
1-3 July 2015
Firstpage :
2001
Lastpage :
2006
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.
Keywords :
genetic algorithms; stock control; supply chains; black-box function; fast simulation-based optimization method; general supply chain networks; genetic algorithm; inventory control; kriging method; objective value; operational efficiency; regional surrogate based framework; service level constraints; trust-region framework; Computational modeling; Genetic algorithms; Optimization; Predictive models; Stochastic processes; Supply chains; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
Type :
conf
DOI :
10.1109/ACC.2015.7171027
Filename :
7171027
Link To Document :
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