Designing an intelligent learner genetic-fuzzy model in oil industry supply chain based on self organized maps

AKHGAR, Babak, SHERKAT, Mhammad Hosein and HANAFIZADEH, Payam (2011). Designing an intelligent learner genetic-fuzzy model in oil industry supply chain based on self organized maps. International Journal of Computational Intelligence: Theory and Practice, 6 (1), 11-27.

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Abstract

Facing the challenges and requirements of supply chain at the present time has led managers to seek for modern methods for tackling the supply chain management. These strategies which are of high flexibility are able to adjust their programs with the real conditions and in the case of need help the decision makers. In the SCM, distribution and allocation problems are of enormous significance and due to their applications in the cross-functional and final parts of SCM problems, they are in a particular position among the SCM problems. In this paper, to provide learning capability and cope with uncertain conditions in SCM, a model is proposed to tackle such problems as the uncertain parameters in Distribution Systems and the need for flexibility in Distribution Systems which can be considered as challenges and designing requirements in an agile model.

Item Type: Article
Research Institute, Centre or Group: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Depositing User: Rachel Finch
Date Deposited: 28 Sep 2012 10:37
Last Modified: 30 May 2013 14:25
URI: http://shura.shu.ac.uk/id/eprint/6383

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