A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization

Abstract : Nowadays, in a hotly competitive environment, companies are continuously trying to provide products and/or services to customers faster, cheaper, and better than the competitors do. Managers have learned that they cannot do it alone; rather, they must work on a cooperative basis with other organizations in order to succeed. Although the resulting enterprise networks are more competitive, the tasks for planning, management and optimization are much more difficult and complex. In this paper, we present a newly developed toolbox "ONE" to support decision makers for the assessment, design and improvement of such supply chain networks. The toolbox comprises innovative and user-friendly concepts related to the modeling, simulation and optimization of modern enterprise networks. Two case studies, proposed by partners from automotive and textile industries, are presented and computational results analysed.
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Article dans une revue
Engineering Applications of Artificial Intelligence, Elsevier, 2006, 19 (6), pp.609-623. 〈10.1016/j.engappai.2005.12.008〉
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https://hal-emse.ccsd.cnrs.fr/emse-00449373
Contributeur : Andrée-Aimée Toucas <>
Soumis le : jeudi 21 janvier 2010 - 14:23:55
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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Hongwei Ding, Lyès Benyoucef, Xiaolan Xie. A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Engineering Applications of Artificial Intelligence, Elsevier, 2006, 19 (6), pp.609-623. 〈10.1016/j.engappai.2005.12.008〉. 〈emse-00449373〉

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