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A Supply Chain Design Problem Integrated Facility Unavailabilities Management
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Fouad Maliki *   1, Mustapha Anwar Brahami2 , Mohammed Dahane3 , Zaki Sari4 |
1- Preparatory school on sciences and techniques, MELT Laboratory, Tlemcen, Algeria , f.maliki@epst-tlemcen.dz
2- Preparatory school on sciences and techniques, MELT Laboratory, Tlemcen, Algeria
3- Ecole Nationale d’Ingénieurs de Metz (ENIM), Laboratoire de Génie Industriel et de Production de Metz (LGIPM), Metz, France
4- Abou Bekr Belkaid University, MELT Laboratory, Tlemcen, Algeria |
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Abstract: (3714 Views) |
| A supply chain is a set of facilities connected together in order to provide products to customers. The supply chain is subject to random failures caused by different factors which cause the unavailability of some sites. Given the current economic context, the management of these unavailabilities is becoming a strategic choice to ensure the desired reliability and availability levels of the different supply chain facilities. In this work, we treat two problems related to the field of supply chain, namely the design and unavailabilities management of logistics facilities. Specifically, we consider a stochastic distribution network with consideration of suppliers' selection, distribution centres location (DCs) decisions and DCs’ unavailabilities management. Two resolution approaches are proposed. The first approach called non-integrated consists on define the optimal supply chain structure using an optimization approach based on genetic algorithms (GA), then to simulate the supply chain performance with the presence of DCs failures. The second approach called integrated approach is to consider the design of the supply chain problem and unavailabilities management of DCs in the same model. Note that, we replace each unavailable DC by performing a reallocation using GA in the two approaches. The obtained results of the two approaches are detailed and compared showing their effectiveness. |
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| Keywords: Supply Chain Management, Location-Allocation, Suppliers Selection, Genetic Algorithms |
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ePublished: 2017/09/28
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