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 - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Page Range: 11-27
    Depositing User: Users 56 not found.
    Date Deposited: 28 Sep 2012 09:37
    Last Modified: 13 Jun 2017 13:03
    URI: http://shura.shu.ac.uk/id/eprint/6383

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