Publications Scientifiques

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Author: search.filters.author.Amodeo, L.Has files: Yes

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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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    Optimization of natural gas pipeline transportation using ant colony optimization
    (2009) Chebouba, A.; Yalaou, F.; Smati, A.; Amodeo, L.; Younsi, A.; Tairi, A.
    In this paper, an ant colony optimization (ACO) algorithm is proposed for operations of steady flow gas pipeline. The system is composed of compressing stations linked by pipelegs. The decisions variables are chosen to be the operating turbocompressor number and the discharge pressure for each compressing station. The objective function is the power consumed in the system by these stations. Until now, essentially gradient-based procedures and dynamic programming have been applied for solving this no convex problem. The main original contribution proposed, in this paper, is that we use an ACO algorithm for this problem. This method was applied to real life situation. The results are compared with those obtained by employing dynamic programming method. We obtain that the ACO is an interesting way for the gas pipeline operation optimization
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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