Stability analysis of the supply chain by using neural networks and genetic algorithms

Authors:
Alfonso Sarmiento;Luis Rabelo;Ramamoorthy Lakkoju;Reinaldo Moraga
Affiliations:
University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;Northern Illinois University, De Kalb, IL
Venue:
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Year:
2007

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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.