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    Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
    (Springer, 2022) Mellal, Mohamed Arezki; Laifaoui, Chahinaze; Ghezal, Fahima; Williams, Edward J.
    The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared
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    A game theory approach to solve linear bi-objective programming problems
    (2017) Bezoui, Madani; Bounceur, Ahcène; Euler, Reinhardt; Moulaï, Mustapha; Djeddi, Youcef
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    System reliability and cost optimization under various scenarios using NSGA-III
    (IEEE, 2020) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel
    Nowadays, industrial systems need to be as reliable as possible in order to ensure safety and competitiveness. This paper addresses the reliabilityredundancy allocation problem (RRAP) of an overspeed protection system in a power plant under various scenarios. Previously, this kind of optimization problems were solved using mathematical programming techniques and considered as a single objective optimization problem, however more recently, bio-inspired algorithms are used to solve this type of optimization problem. In the present work, a multi-objective evolutionary optimization algorithm, called the non-dominated sorting genetic algorithm (NSGA-III) is implemented to solve the problem under a set of nonlinear design constraints. The NSGA-III demonstrates its ability to generate a set of nondominated solutions. The results are discussed under various scenarios of minimum allowable reliability
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    Cost and Availability optimization of Overspeed Protection System in a Power Plant
    (IEEE, 2019) Mellal, Mohamed Arezki; Chebouba, Billal Nazim
    This paper addresses the cost and availability optimization of an overspeed protection system in a power plant. The literature has only treated the reliability or cost of this system as a single-objective. Therefore, the multi-objective optimization problem considering the availability and cost is presented. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to generate the Pareto front. The numerical results are discussed under two scenarios of minimum allowable availability.
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    An efficient methodology for multi-objective optimization of water alternating CO2 EOR process
    (Elsevier, 2019) Nait Amar, Menad; Zeraibi, Noureddine
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    Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization
    (Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine
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    A game theory approach to solve linear bi-objective programming problems : application to data collection in WSNs
    (2017) Bezoui, Madani; Bounceur, Ahcène; Euler, Reinhardt; Moulai, Mustapha
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    Bi-objective optimization algorithms for joint production and maintenance scheduling : application to the parallel machine problem
    (Springer, 2009) Berrichi, A.; Amodeo, L.; Yalaoui, F.; Châtelet, E.; Mezghiche, Mohamed
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    Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem
    (Springer, 2013) Berrichi, Ali; Yalaoui, Farouk
    In recent years, decision makers give more im- portance to the maintenance function, viewing its substantial contribution to business productivity. However, most litera- ture on scheduling studies does not take into account main- tenance planning when implementing production schedules. The achievement of production plan without taking into account maintenance activities increases the probability of machine breakdowns, and inversely, considering mainte- nance actions in production planning elongates the achieve- ment dates of orders and affects deadlines. In this paper, we propose a bi-objective model to deal with production sched- uling and maintenance planning problems simultaneously. The performance criteria considered for production and maintenance are, respectively, the total tardiness and the unavailability of the production system. The start times of preventive maintenance actions and their number are not fixed in advance but considered, with the execution dates of production tasks, as decisions variables of the problem. The solution of the integrated model is based on multi-objective ant colony optimization approach. The proposed algorithm (Pareto ant colony optimization) is compared, on the basis of several metrics, with well-known multi-objective genetic algorithms, namely NSGA-II and SPEA 2, and a hybrid particle swarm optimization algorithm. Interesting results are obtained via empirical study
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    Bi-objective ant colony optimization approach to optimize production and maintenance scheduling
    (Elsevier, 2010) Mezghiche, Mohamed; Amodeo, L.; Yalaoui, F.; Berrichi, A.
    This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem .This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi- objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take in to account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method out performs two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2and NSGAII