Title

Stability Analysis Of The Supply Chain By Using Neural Networks And Genetic Algorithms

Abstract

Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology. © 2007 IEEE.

Publication Date

12-1-2007

Publication Title

Proceedings - Winter Simulation Conference

Number of Pages

1968-1976

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WSC.2007.4419826

Socpus ID

49749108209 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/49749108209

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