@article {ElAmrani2019109,
	title = {Multi-capacitated Location Problem: A New Resolution Method Combining Exact and Heuristic Approaches Based on Set Partitioning},
	journal = {Studies in Computational Intelligence},
	volume = {774},
	year = {2019},
	note = {cited By 0},
	pages = {109-121},
	doi = {10.1007/978-3-319-95104-1_7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053069722\&doi=10.1007\%2f978-3-319-95104-1_7\&partnerID=40\&md5=08ad9adabe61d8494d5b09f76263829e},
	author = {El Amrani, M. and Benadada, Y. and Gendron, B.}
}
@conference {Hamada20172084,
	title = {A hybrid approach for a strategic planning problem represented by a three-echelon logistics network},
	booktitle = {Proceedings of the International Conference on Industrial Engineering and Operations Management},
	year = {2017},
	note = {cited By 0},
	pages = {2084-2085},
	abstract = {This article aims to elaborate a strategic plan allowing to decision makers to take right decisions (Selecting suppliers, Selecting plants that can produce a specific product,..) in the right moment in order to minimize the generated costs It is in this context that our work is being taken. The objective is to model and solve a strategic planning problem at three levels which are: supplier-plant, plant-plant and plant-customer. The costs taken into effect in this model are: the supply costs, production costs and transport costs. The problem belongs to the CFLP Family (Capacitated Facility Location Problem) with a NPhard complexity. The objective of our MIP problem is to maximize a production company benefits through minimizing costs mentioned above. Given the limitations of exact methods and the optimum local problem when we used a heuristic method like a LNS (Large Neighborhood Search), we proposed to solve this problem using hybridization between two heuristics: LNS \& Genetic Algorithm This hybridization will give us more opportunities to find better solution when a local optimum is reached. {\textcopyright} IEOM Society International.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018996488\&partnerID=40\&md5=986399337bcb183f863d9133bfa92170},
	author = {Hamada, Y. and Benadada, Y. and Gendron, B.}
}
@conference {ElAmrani20171973,
	title = {New exact method to solve multi-capacitated location problem using set partitioning formulation},
	booktitle = {Proceedings of the International Conference on Industrial Engineering and Operations Management},
	year = {2017},
	note = {cited By 0},
	pages = {1973-1982},
	abstract = {In this paper, we present one generalization of the famous capacitated p-median location problem, called budget constraint multi-capacitated location problem (MCLP). This generalization is characterized by allowing each facility to be used with different capacity levels. We consider n customers, m facilities and l capacity levels, we note that the solution shape of MCLP can be represented as a set of disjoint clusters, each cluster is composed of one facility and a subset of customers. When creating clusters, some constraints must be met, namely the level selection and capacity. In this work, we present the new formulation of the MCLP based on set partitioning, then we suggest an adapted solving method, which will be called NFF (Nearest Facility First). The NFF approach is used in two ways: as a heuristic by taking only the first solution found or exact method when waiting finish the execution. Computational results are presented at the end using instances that we have created under some criteria of difficulties or adapted from those of p-median problems available in literature. The NFF method provides very good results for low and medium difficulty instances, but it is less effective for the more complex ones. To remedy this problem, the method will be supplemented by column generation approach. {\textcopyright} IEOM Society International.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018949470\&partnerID=40\&md5=4b843bd615c5260fc757d470ecea56ca},
	author = {El Amrani, M. and Benadada, Y. and Gendron, B.}
}
