Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Obsolescence optimization of electronic and mechatronic components by considering dependability and energy consumption(Springer, 2013) Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel; Berrazouane, Sofiane; Williams, EdwardItem Optimal policy for the replacement of industrial systems subject to technological obsolescence using genetic algorithm(2013) Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel; Berrazouane, Sofiane; Williams, Edward J.The technological obsolescence of industrial systems is characterized by the existence of challenger units possessing identical functionalities but with improved performance. This paper aims to define a new approach that makes it possible to obtain the optimal number of obsolete industrial systems which should be replaced by new-type units. This approach presents a new point of view compared with previous works available in the literature. The main idea and the originality of our approach is that we apply a genetic algorithm (GA) by considering the failure frequency, the influence of the environment/safety factors of the old-type systems and the purchase/implementation cost of the new-type units. These parameters are introduced in order to optimize this type of replacement in the context of engineeringItem Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system(Elsevier, 2014) Berrazouane, Sofiane; Mohammedi, K.Item Genetic algorithm for multiobjective optimization : applied in high speed machining milling operation(2012) Mokhtari, Hicham; Ouziala, Mahdi; Mellal, Mohamed Arezki; Belaidi, Idir; Alem, Said; Berrazouane, SofianeGenetic Algorithms (GAs) are general-purpose heuristic search algorithms that mimic the evolutionary process in order to find the fittest solutions. The algorithms were introduced by Holland in 1975. Since then, they have received growing interest due to their ability to discover good solutions quickly for complex searching and optimization problems. Simple genetic algorithms have been developed to solve the problems of multi objective optimization, such as NSGA II. The objective of this research is to apply the elitist non-dominated sorting GA (NSGA-II) for multi-objective optimization problems in case of high speed machining for the milling operation. The implemented model under Matlab, allows, from a considered space research. We have optimized the values of Vc and f, for an imposed Depth, while the production cost and time are minimized, under technical constrains of the production system