International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

Neuro-fuzzy decision learning on supply chain configuration

J. C. Garcia Infante1, J. J. Medel Juarez2 and J. C. Sanchez Garcia1*
1Mechanical and Electrical Engineering School IPN, Col. San Francisco Culhuacan Del.Coyoacán, D. F. ext.73092 Mexico. 2Computing Research Centre, Av. 100 m, esq., Venus, Col. Nueva Industrial Vallejo, C. P. 07738 D. F. ext. 56570 Mexico
Email: [email protected]

  •  Accepted: 01 July 2013
  •  Published: 16 July 2013


This paper describes the computational automatic supply chain configuration (SCC) based on fuzzy logic prediction actualizing automatically the chain stages considering different customer service level petitions. Each level is selected in accordance with the inference and the knowledge base process supplies (KBPS). The SCC model as an intelligent processes selector (IPS), allows dynamical configuration in accordance with the minimum cost supplies configuration (MCSC) described with the SCC functional error. The basic future decisions set as a knowledge base (KB) using fuzzy rules and inferences, transforms the proposed decisions into actions over the elements required by the process supply (PS). The minimal functional error and the SCC best selection, permits excellent client attention. The adaptive model stages operational SCC is described illustratively using Matlabâ software.


Key words: Fuzzy digital learning, neural networks, supply chain configuration.