Publications Internationales

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Subject: search.filters.subject.Multi-objective optimization

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Now showing 1 - 8 of 8
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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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    Optimization of WAG Process Using Dynamic Proxy, Genetic Algorithm and Ant Colony Optimization
    (Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Kheireddine, Redouane
    The optimization of water alternating gas injection (WAG) process is a complex problem, which requires a significant number of numerical simulations that are time-consuming. Therefore, developing a fast and accurate replacing method becomes a necessity. Proxy models that are light mathematical models have a high ability to identify very complex and non-straightforward problems such as the answers of numerical simulators in brief deadlines. Different static proxy models have been used to date, where a predefined model is employed to approximate the outputs of numerical simulators such as field oil production total (FOPT) or net present value, at a given time and not as functions of time. This study demonstrates the application of time-dependent multi Artificial Neural Networks as a dynamic proxy to the optimization of a WAG process in a synthetic field. Latin hypercube design is used to select the database employed in the training phase. By coupling the established proxy with genetic algorithm (GA) and ant colony optimization (ACO), the optimum WAG parameters, namely gas and water injection rates, gas and water injection half-cycle, WAG ratio and slug size, which maximize FOPT subject to some time-depending constraints, are investigated. The problem is formulated as a nonlinear optimization problem with bound and nonlinear constraints. The results show that the established proxy is found to be robust and an efficient alternative for mimicking the numerical simulator performances in the optimization of the WAG. Both GA and ACO are strongly shown to be highly effective in the combinatorial optimization of the WAG process.
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    Multiobjective optimization of an agile machining type linear delta structure
    (2018) Mansouri, Khaled; Belaidi, Idir; Atia, Abdelmalek
    The innovative architectures design of agile machines dedicated to the machining at high speed requires the implementation of analytical and numerical models for the optimization of the kinematic, static and dynamic behavior of the machine, taking into account the elastic deformations and their compensation at level of machine control. In the context of multi-objective optimization, the first part is to identify the parameters and variables inherent to each constituent element of a DELTA robot type machine, the purpose is to optimize the essential elements of its structure. This requires a formulation of the multi-objective problem by expressing the objective functions, the constraints and the corresponding search spaces, as well as the resolution of the problem by the use of high-performance mathematical methods and tools (genetic algorithms, etc.).
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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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    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