Labbi, O. and Ouzizi, L. and Douimi, M. and Ahmadi, A. (2016) A multi-objective evolutionary approach for simultaneous suppliers' selection and product design. In: UNSPECIFIED.

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This paper deals with a recent approach that tackles the product and the supply chain design issues at the same time and focuses more precisely on the supplier selection problem. The product specificities and the constraints of suppliers are both integrated into product design phase. Usually, the supplier selection problem involves multiple and conflicting objectives. In this work, a multi objective model is formulated to minimize supplying and holding costs of product components and quality rejected items. We study the case of an existent product redesign and we address the problem of supplier selection. Design team proposes several alternatives to improve the product and the aim is to select the best product design with its optimal set of components' suppliers. Thus, a multi-objective programming model for supplier selection is developed. Picking a set of Pareto front for multi-objective optimization problems require robust and efficient methods that can search an entire space. To solve the model, we used a non dominant sorting genetic algorithm (NSGA-II). A numerical experiment is provided at the end to show the applicability of the methodology and to compare it with the simple weighed sum method. © 2016 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Evolutionary algorithms; Genetic algorithms; Multiobjective optimization; Numerical methods; Optimization; Supply chains, Conflicting objectives; Multi-objective evolutionary; Multi-objective modeling; Multi-objective optimization problem; Multiobjective programming; Numerical experiments; Sorting genetic algorithm; Supplier selection, Product design
Subjects: Business, Management and Accounting
Divisions: SCIENTIFIC PRODUCTION > Business, Management and Accounting
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:44

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