Dynamic scheduling of maintenance activities under uncertainties.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the dierent practical knowledges or skills are their working tools. We address, in this paper, the both assignment and scheduling problem that can be found in a maintenance service. Each task that has to be performed is characterized by a competence level required. Then, the decision problem of assignment and scheduling lead to find the good resource and the good time to do the task. For human resources, all competence levels are dierent, they are considered as unrelated parallel machines. Our aim is to assign dynamically new tasks to the adequate resources by giving to the maintenance expert a choice between the robustest possibilities

Static et dynamic scheduling of maintenance activities under the constraints of skills.

International audienceSkill management in industry is one of the most important factors required in order to obtain optimal performance of the production system. This is of particular importance in the field of maintenance where the different practical knowledge or skills are the working tools used. We address, in this paper, both the assignment and scheduling problems that may be found in a maintenance service. Each task that has to be performed is characterized by the level of skill required. The problem lies with making the decision of which time is the right time for the assignment and scheduling of the correct resource to do the task. We introduce both static and dynamic scheduling, applied to the maintenance task assignment. To confer a maximum robustness to the obtained schedule, tha approach proposed in this paper is completed by a proactive methodology which takes into account possible variations

Ordonnancement des activités de maintenance sous contraintes de compétences.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the different practical knowledges or skills are their working tools. We propose here a methodology, which compares the human resource with parallel machine. As human ressource competence levels of each are all different, they are considered like unrelated parallel machines. Our aim is to assign tasks to the adequate resources by minimizing time treatment for each task and the makespan

Ré-ordonnancement partiel et dynamique d'un planning d'activités de maintenance.

International audienceLa gestion de compétences dans l'industrie est l'une des clefs les plus importantes pour obtenir le meilleur des moyens de production, particulièrement dans le domaine de la maintenance où les différentes connaissances et qualifications sont les outils de travail du personnel. Nous traitons dans cet article des problèmes d'affectation et d'ordonnancement que l'on peut rencontrer dans un service de maintenance. Chacune des tâches qui doivent être réalisées est caractérisée par une compétence requise. La résolution du problème d'affectation et d'ordonnancement nous conduira donc à trouver la bonne ressource et la bonne date de traitement de la tâche. Notre but est d'affecter dynamiquement la charge aux ressources adéquates en donnant à l'expert du service de maintenance le choix entre les solutions les plus intéressantes

Maintenance activities scheduling under competencies constraints.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the different practical knowledges or skills are their working tools. We propose here a methodology, which compares the human resource with parallel machine. As human resource competence levels of each are all differents, they are considered like unrelated parallel machines. Our aim is to assign tasks to the adequate resources by minimizing time treatment for each task and the makespan

: Habilitation à Diriger des Recherche

Chaque projet est unique, de par le degré de nouveauté qu'il présente tant au niveau du produit que des activités et de l’organisation qu’il mobilise. Pour répondre à un même besoin une variété de technologies, de processus de réalisation, de parties prenantes et d’acteurs peut être choisie. De plus, différents risques peuvent survenir au cours du déroulement des activités. Ainsi, parmi un nombre important de déroulés possibles, un seul scenario de projet sera réalisé a posteriori. Les responsables doivent donc concevoir et piloter le projet afin que le scénario réalisé tienne les engagements fixés. Des décisions doivent donc être prises progressivement suivant les axes classiques Produit, Process, Ressource. Ces travaux ont pour originalité de considérer, en complément, l'axe décisionnel du Risque portant sur le traitement des risques, mais aussi les interrelations entre les décisions de chaque axe. Pour aider à la décision, les contributions apportées sont des concepts, modèles, approches et outils permettant de choisir les stratégies de traitement du risque, les activités du projet et les ressources

Robustness measure for fuzzy maintenance activities schedule.

International audienceSkills management in industry is one of the most important factors in order to obtain good performance with production means. Especially in the field of maintenance services where the different practical knowledge or skills are their working tools. We address, in this paper, both the assignment and scheduling problems that can be found in a maintenance service. Each task that has to be performed is characterized by the level of skill required. The problem lies with making the decision of which time is the right time for the assignment and scheduling of the correct resource to do the task. For human resources, all skill levels are different, they are considered as unrelated parallel machines. Our aim is to assign new tasks to the adequate resources by giving to the maintenance expert a good and robust possibility

Proactive, dynamic and multi-criteria scheduling of maintenance activities.

International audienceIn maintenance services skills management is directly linked to the performance of the service. A good human resource management will have an effect on the performance of the plant. Each task which has to be performed is characterised by the level of competence required. For each skill, human resources have different levels. The issue of making a decision about assignment and scheduling leads to finding the best resource and the correct time to perform the task. The solve this problem, managers have to take into account the different criteria such as the number of late tasks, the workload or the disturbance when inserting a new task into an existing planning. As there is a lot of estimated data, the managers also have to anticipate these uncertainties. To solve this multi-criteria problem, we propose a dynamic approach based on the kangaroo methodology. To deal with uncertainties, estimated data is modelled with fuzzy logic. This approach then offers the maintenance expert a choice between a set of the most robust possibilities

A collaborative demand forecasting process with event-based fuzzy judgements

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy