A knowledge-based discrete event simulation approach for supplier selection with order allocation

This paper presents a novel approach for automatic fuzzy based knowledge acquisition, which clubs supplier selection process with order allocation for dynamic supply chains to cope up with market variations. It imitates the knowledge acquisition and manipulation in a manner similar to that of decisi...

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Bibliographic Details
Published in:	2009 IEEE International Conference on Industrial Engineering and Engineering Management pp. 1673 - 1678
Main Authors:	Zouggari, A., Benyoucef, L., Jain, V.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2009
Subjects:	Artificial intelligence Competitive intelligence Current supplies Discrete event simulation Humans Knowledge Knowledge acquisition Mechanical engineering Quality of service simulation supplier selection Supply chains Technological innovation
Online Access:	Get full text
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Summary:	This paper presents a novel approach for automatic fuzzy based knowledge acquisition, which clubs supplier selection process with order allocation for dynamic supply chains to cope up with market variations. It imitates the knowledge acquisition and manipulation in a manner similar to that of decision makers who has gathered considerable knowledge and expertise in procurement domain. According to this concept, those decision criteria for supplier selection are considered first through four classes (CLASS I: Performance strategy, CLASS II: Quality of service, CLASS III: Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using fuzzy logic, the criteria application is quantitatively evaluated. As a result, the proposed approach generates knowledge for decision-making, and then, the developed combination of rules for supplier selection with order allocation can easily be interpreted, adopted and if necessary, modified by supply chain decision makers/practioners.
ISBN:	1424448697 9781424448692
ISSN:	2157-3611 2157-362X
DOI:	10.1109/IEEM.2009.5373133