Optimisation hybride par colonies de fourmis pour le problème de découpe à deux dimensions

Nous nous intéressons dans cet article au problème de découpe guillotine en deux dimensions noté 2BP/O/G. Il s'agit de découper un certain nombre de pièces rectangulaires dans un ensemble de plaques de matière première, elles même rectangulaires et identiques. Celles-ci sont disponibles en quantité illimitée. L'objectif est de minimiser le nombre de plaques utilisées pour satisfaire la demande, en appliquant une succession de coupes, dites guillotines, allant de bout en bout. Nous proposons une approche de résolution combinant l'optimisation par colonies de fourmis (ACO) et l'heuristique SHF-FF de Ben Messaoud et al. [2] pour résoudre ce problème NP-difficile

Optimisation de laboratoires médicaux

This thesis focuses on the optimization of clinical laboratory design and operating decisions. A clinicallaboratory is an organization gathering human and machinery resources to analyze blood samples. Inthis thesis, a decision support tool including mathematical models, a heuristic algorithm and acustomized simulation model is developed to aid decision makers for the main strategic, tactical andoperational problems in clinical laboratory design and operations management. This decision supporttool follows a top-down stepwise framework starting from strategic problems and ending withoperational ones, including a recursive loop for modification and improvement. In this thesis, machineselection and facility layout are studied as the main strategic problems, analyzer configuration problemas the tactical problem, and assignment, aliquoting, and scheduling as the principal operationalproblems. In order to deal with machine selection problem for clinical laboratory, a mathematical modelis proposed which aids to select the most appropriate machines to equip the system. To tackle physicalarrangement of instruments within the laboratory area, a heuristic approach is developed. The proposedheuristic comprises the key constraints of laboratory layout design. To address the analyzerconfiguration problem which mainly deals with the assignment of chemical materials to the analyzersin clinical laboratory, a bi-objective mathematical model is developed. In addition, to determine anefficient assignment of sample tubes to the analyzers, a mathematical model with three objectives isproposed. A customized, flexible, and fine-grained simulation model is developed in FlexSim to studythe clinical laboratory designed through the outputs of developed mathematical models and layoutalgorithm. Simulation model plays a key role in the proposed framework as it is used for many purposes.The simulation model helps the designer to construct and analyze a complete clinical laboratory takinginto account all major features of the system. This simulation attribute provides the ability to scrutinizethe system behaviour and to find out whether the designed system is efficient. System performanceanalysis through simulation and resulting key performance indicators give helpful feedbacks for systemimprovement. Furthermore, simulation model can be fruitful to decide on scheduling, aliquoting andstaffing problems through the evaluation of various scenarios proposed by decision maker for each ofthese problems. To verify the validity of the proposed framework, data extracted from a real case isused. The output results seal on the applicability and the efficiency of the proposed framework as wellas competency of proposed techniques to deal with each optimization problem. To the best of ourknowledge, this thesis is one of the leading studies on the optimization of clinical laboratories.Cette thèse porte sur l'optimisation de la conception et des décisions opérationnelles des laboratoires d'analyses médicales. Dans cette thèse, un outil d'aide à la décision comprenant des modèles mathématiques, un algorithme heuristique et un modèle de simulation personnalisé est développé pour aider les décideurs à résoudre les principaux problèmes stratégiques, tactiques et opérationnels en conception et gestion des opérations des laboratoires d'analyses médicales. Dans cette thèse, la sélection des machines et la disposition des instruments sont étudiées en tant que principaux problèmes stratégiques, le problème de configuration des analyseurs en tant que problème tactique et l’affectation, l’aliquotage et l'ordonnancement en tant que principaux problèmes opérationnels. Un modèle de simulation personnalisé et flexible est développé dans FlexSim pour étudier le laboratoire d'analyse médicale conçu à l'aide des résultats de modèles mathématiques et d'un algorithme de layout développés. Le modèle de simulation aide le concepteur à construire et à analyser un laboratoire complet en tenant compte de toutes les principales caractéristiques du système. Cet attribut de simulation permet d'analyser le comportement du système et de déterminer si le système conçu est efficace. Pour vérifier la validité du cadre proposé, les données extraites d’un cas réel sont utilisées. Les résultats de sortie scellent l'applicabilité et l'efficacité du cadre proposé ainsi que la compétence des techniques proposées pour traiter chaque problème d'optimisation. À notre connaissance, cette thèse est l’une des principales études sur l’optimisation des laboratoires d'analyses médicales

Fuzzy Rules for Joint Integration of Production Schedule and Maintenance Planning

International audienceThe relationship between production and maintenance has always been considered as a conflict in management decision. Most studies dealing with this problem adopt the approach commonly called “scheduling with availability constraints” [1] [2], where a fixed number of preventive maintenance (PM) activities are planned first then the scheduling of production jobs is optimized considering the PM actions as constraints. Recently, integrated models have been proposed to deal with the two activities simultaneously [3] [4] [5]. Multi objective techniques have been developed to find trade-off solutions to the problem. However, one of the important issues which can influence the quality and efficiency of the obtained solutions is the insertion strategy of production jobs and PM activities to avoid conflicts and gain efficiency. In this paper, we study four types of priority rules to optimize both criteria of production and maintenance simultaneously in parallel machine shop. Rules based on fuzzy logic are proposed and compared to other crisp strategies. Computational results based on a multi objective genetic algorithm show that scheduling rules based on fuzzy logic are effective

Fuzzy rules for joint integration of production schedule and maintenance planning

International audienceThe relationship between production and maintenance has always been considered as a conflict in management decision. Most studies dealing with this problem adopt the approach commonly called “scheduling with availability constraints” [1] [2], where a fixed number of preventive maintenance (PM) activities are planned first then the scheduling of production jobs is optimized considering the PM actions as constraints. Recently, integrated models have been proposed to deal with the two activities simultaneously [3] [4] [5]. Multi objective techniques have been developed to find trade-off solutions to the problem. However, one of the important issues which can influence the quality and efficiency of the obtained solutions is the insertion strategy of production jobs and PM activities to avoid conflicts and gain efficiency. In this paper, we study four types of priority rules to optimize both criteria of production and maintenance simultaneously in parallel machine shop. Rules based on fuzzy logic are proposed and compared to other crisp strategies. Computational results based on a multi objective genetic algorithm show that scheduling rules based on fuzzy logic are effective

Integrated production planning and preventive maintenance in deteriorating production systems

International audienceThe traditional production planning model based upon the famous linear programming formulation has been well documented. However, the integration of preventive maintenance planning in the same model is a recent problem. This paper proposes an extended linear programming model as a hybrid approach for computing the optimum production plan with minimum total cost. The dual objective problem of production planning and maintenance is treated into a mixed integer linear program. This program is not only considering cases of multi-lines, multi-periods and multi-items but also taking into account the deterioration of the lines. This deterioration is represented in the model as a reduction of production lines capacities in function of the time evolution. Maintenance operations are supposed to provide lines in an operational state as good as new, i.e. with a maximum capacity. Through the study of the models limits, it is shown that the proposed approach can deal with a broader range of problems than that of Aghezzaf and Najid (2008) [3]. An optimal relaxation technique based on the polyhedral theory is developed to improve the computational time and expand the limits of the proposed model. Also, a “Fix and Relax heuristic” is developed for complex problems. Their computation time and their difference are computed referring to the same lower bound and the same considerations as those presented by Aghezzaf and Najid. It is proved through more than 880 several simulations for each model with different capacities and different setup costs, that this approach can solve large size problems with moderate computational time and gap

Ordonnancement sur machines parallèles avec équilibrage des charges : analyse des différents critères

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Design and Layout

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Scheduling

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Optimisation de la conception des lignes de production

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