A simulation optimization methodology for supplier selection problem

Abstract : Strategic sourcing plays a critical role in supply chain planning. Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: a genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.
Type de document :
Article dans une revue
International Journal of Computer Integrated Manufacturing, Taylor & Francis, 2005, 18 (2-3), pp.210-224. 〈10.1080/0951192052000288161〉
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https://hal-emse.ccsd.cnrs.fr/emse-00449389
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Soumis le : jeudi 21 janvier 2010 - 14:46:17
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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Hongwei Ding, Lyès Benyoucef, Xiaolan Xie. A simulation optimization methodology for supplier selection problem. International Journal of Computer Integrated Manufacturing, Taylor & Francis, 2005, 18 (2-3), pp.210-224. 〈10.1080/0951192052000288161〉. 〈emse-00449389〉

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