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This paper deals with the bidirectional flow-rack AS/RS (Automated Storage and Retrieval System). This system was recently introduced in the literature. In this paper, models that consider the system from a holistic aspect are presented in order to grasp its real systemic running. The adopted modelling approach allows obtaining closed form and easy to use equations that model the expected required time to carry out storage/retrieval transactions for the whole system. The paper presents derived continuous models for these transaction. These latter are easy to use to perform mathematical investigations, as optimization, throughput evaluation, systems comparison etc. However, they are not completely faithful to the real system behavior. This is why also discrete models were developed, which are much more accurate, and which were used as a benchmark to evaluate the accuracy of the continuous ones. To do so, a self-made software was developed, and results could be extracted and discussed.

The current dynamic economic context with very rapid changes, pushes economic players to always get up tospeed, adapt and innovate to face the growing competition. Having efficient supply chains capable of absorbingthe shocks of various fluctuations is a necessity. Storage warehouses are essential links in these latter, andimproving their performance is the keystone through which the control of any supply chain must pass. Theappearance of automated-storage/retrieval-systems (AS/RS) has made it possible to have the technologicalimplements to achieve these ends. However, it is necessary to have mathematical tools that allow the effectiveand efficient use of these equipment in order to optimize their performances. In this paper, we developmathematical models of dual cycle times for the bidirectional-flow-rack AS/RS. First, we develop modelsaccording to two approaches and then compare them to each other. Then we proceed to an optimization of thesystem's dimensions for minimum cycle times. This allowed us to extract approximate formulas of optimaldimensions according to the size of the system and its nominal load rate. Next, thanks to a numerical study wecould deduce the optimal shape of the system for the two modelled cycle times as a function of the nominalload rate. Lastly, we proceeded to a throughput calculation thanks to the conducted study. This represents anindispensable tool for the upstream design and installation of the system in order to optimize its performancesin steady state, which will have a direct positive impact on the supply chain performances.

Optimizing the design of storage systems is of crucial importance for operational excellence. The aim of this paper is to explore the optimization of the design for automated storage systems. In this latter, we are interested in three different systems. First, we highlight the different methods used to deal with this problem and point out their limits. Thereafter, we present the different corresponding cycle times models. Then, we identify and demonstrate several mathematical properties, which we use as dominance properties to implement an efficient resolution algorithm. Our algorithm greatly surpasses the existing optimization methods used for the design optimization problems. The proposed approach allows us to obtain the optimal solution ten times faster than the commonly used methods, as illustrated by a comparative numerical study.

International audience; In an industrial environment, manufacturing systems may be subject to considerable uncertainties which could lead to numerous schedule disturbances. These disturbances prevent the execution of a manufacturing schedule as it was planned. The "groups of permutable operations" method copes with this drawback by proposing a family of schedules instead of a unique one. However, the selection of the appropriate schedule that accounts for real-time disturbances, represents a combinatorial optimization challenge. In this paper, we propose a new decision-aid criterion for selecting the schedule that fits best the real state of the shop. This criterion is measured using a greedy heuristic that anticipates the maximum tardiness in a job shop scheduling environment. Simulation tests performed on benchmark problems show the usefulness of the proposed criterion compared to another frequently used criterion. The final results emphasize the usefulness of this criterion in a bi-criteria decision-aid system.

International audience

Ant colony algorithms are computational methods inspired from the behavior of real ant colonies. In this paper our interest is focused on the adaptation of an optimization algorithm called API based on the foraging behavior model of primitive ants’ population called Pachycondyla apicalis to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS) with and without presence of breakdowns. The FMS consists of seven machining centers, a loading and an unloading stations, and six different part types. Owing to the presence of identical machining centers, the parts have alternative routings. The scheduling decisions has been established in terms of how the parts are routed through various machines in the system.

International audience; Ce papier présente une stratégie de maintenance sélective pour un système multi-composants requis pour effectuer une série de missions avec des arrêts finis planifiés entre deux missions successives. Pendant ces arrêts, les actions de maintenance peuvent être réalisées sur certains composants du système. Pour chaque composant, une liste d'actions de maintenance est disponible où figurent les actions de maintenance parfaite, minimale ou imparfaite. Tenant compte des limitations sur les ressources en maintenance telles que le temps et le budget, il est parfois impossible d'effectuer toutes les opérations de maintenance désirées. Le problème de la maintenance sélective vise donc à sélectionner les composants qui doivent être maintenus afin de maximiser la fiabilité du système pour exécuter la prochaine mission. Dans ce travail, les durées des missions sont considérées aléatoires et représentées par des variables aléatoires. Un modèle d'optimisation mathématique de la maintenance sélective est ensuite proposé et dont l'objectif est de maximiser la fiabilité du système à exécution sa prochaine mission, en tenant compte des contraintes de budget et du temps alloués à la maintenance. L'intérêt de notre approche est démontré sur un exemple de système séries-parallèle.

International audience; Nous traitons, dans ce papier, un problème d'ordonnancement dans un environnement de production flow shop de permutation sous contrainte de ressource non-renouvelable (matière première). Dans ce type de problème, les ressources nécessaires à l'exécution d'une tâche peuvent être consommées comme elles peuvent être indisponibles pour des raisons diverses, par conséquent, le manque de ressources perturbe et affecte l'efficacité de l'ordonnancement. Un état de l'art a été réalisé, ce dernier a pour objectif d'expliquer les choix théoriques qui constituent la base de notre problématique et qui ont guidé nos recherches. Par la suite, nous proposons un modèle linéaire en nombres entiers pour minimiser la durée totale d'ordonnancement. Ce problème est NP- difficile au sens fort