Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, andit permits a natural co-habitation between infinite capacity scheduling processes, performedby the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop

Editorial of the Special Section: Anticipation of risks impacts and industrial performance evaluation in distributed organizations life cycles

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Component-based simulation for a reconfiguration study of transitic systems

This paper is organized as follows. Part A presents the context of reconfiguring transitic systems and the main idea in implementing the decision step. It comprises sections 1 to 3. Section 3 presents an example that illustrates the concepts presented in the next sections. Parts B and C express the models and principles used to simulate transitic systems, the result of which will be helpful for choosing the new configuration. Part B focuses mainly on models. It comprises sections 4 to 6. Part C focuses mainly on simulation principles. It comprises sections 7 to 10

Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

Coupling system design and project planning: discussion on a bijective link between system and project structures

This article discuss the architecture of an integrated model able to support the coupling between a system design process and a project planning process. The project planning process is in charge of defining, planning and controlling the system design project. A benchmarking analysis carried out with fifteen companies belonging to the world competitiveness cluster, Aerospace Valley, has highlighted a lack of models, processes and tools for aiding the interactions between the two environments. We define the coupling as the establishment of links between entities of the two domains while preserving their original semantic, thus allowing information to be collected. The proposed coupling is recursive. It enables systems to be decomposed into subsystems when designers consider complexity to be too high, and can also decompose projects into sub-projects. The coupling enables systematically links to be drawn between project entities and system entities. In this paper, we discuss the different possibilities of linking system and project structures during the design and the planning processes. Firstly, after presenting the results of the industrial analysis, the different entities are defined and the various coupling modes are discussed

Technic and Collaboration Breakdown Structures: Drivers of collaborative problem solving approaches in a supply chain context

Problem Solving Methodologies have been par excellence a cornerstone element of the firms’ strategy on achieving effective continuous improvement. But the enterprise evolution towards an extended environment characterized by network-based organization has radically changed the problem solving paradigms. This paper aims to propose a generic and collaborative methodology addressing more complex and distributed problems, dealing with Supply Chain issues and having a key role as a driver for building global competitive advantages and create superior performances at a Supply Chain level

Proposition d’une architecture composée de multiples processus de retour d’expérience coopérants

Cet article présente les premiers résultats d’une étude réalisée en partenariat avec l’entreprise Turbomeca traitant des problèmes engendrés par l’implémentation de processus de retour d’expérience dans une entreprise étendue. La première partie de l’article est dédiée à la définition et à la description des processus de retour d’expérience et des approches les plus avancées pour faciliter leur implémentation. Dans une seconde partie, nous montrons que dans une entreprise étendue, il est nécessaire de définir de multiples processus de retour d’expérience pour que l’approche soit adaptée aux différents niveaux de décisions et aux différents produits/technologies utilisés dans l’entreprise. Nous proposons une trame générale pour intégrer ces différents aspects et nous présentons une illustration d’un cas concret. Finalement, nous concluons sur l’originalité de notre proposition ses avantages et les perspectives de notre travail

Scenario selection optimization in system engineering projects under uncertainty: a multi-objective ant colony method based on a learning mechanism

This paper presents a multi-objective Ant Colony Optimization (MOACO) algorithm based on a learning mechanism (named MOACO-L) for the optimization of project scenario selection under uncertainty in a system engineering (SE) process. The objectives to minimize are the total cost of the project, its total duration and the global risk. Risk is considered as an uncertainty about task costs and task durations in the project graph. The learning mechanism aims to improve the MOACO algorithm for the selection of optimal project scenarios in aSE project by considering the uncertainties on the project objectives. The MOACO-L algorithm is then developed by taking into account ants’ past experiences. The learning mechanism allows a better exploration of the search space and an improvement of the MOACO algorithm performance. To validate our approach, some experimental results are presented

A risk-based approach applied to system engineering projects: a new learning based multi-criteria decision support tool based on an ant colony algorithm

This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system’s conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies

Formalisation et exploitation de connaissances et d’expériences pour l’aide à la décision dans les processus d’ingénierie système

Ce manuscrit d’habilitation à diriger des recherche synthétise mon activité professionnelle en enseignement et en recherche depuis l’obtention de mon poste de maître de conférences en 2001. Après l’obtention de mon diplôme de doctorat, préparé au Laboratoire Génie de Production (LGP) entre 1997 et 2000 sous la direction de Bernard Grabot, j’ai obtenu mon poste de maître de conférences à l’Université de Bretagne Sud à Lorient (UBS). Durant une période de trois années dans cette université et au Laboratoire d’Electronique des Systèmes Temps Réels (LESTER devenu LAB-STICC par la suite), j’ai pu développer des activités de recherche dans le domaine de la conception et de la reconfiguration des systèmes automatisés de type Systèmes Transitiques. Suite à ma mutation à l’Ecole Nationale d’Ingénieurs de Tarbes en 2004, j’ai poursuivi mes activités de recherche au Laboratoire Génie de Production (LGP) en lien avec le développement d’outils d’aide ‘a la décision dans les processus d’ingénierie système basés sur l’exploitation de connaissances et d’expériences. En enseignement, depuis 2001, mes activités sont partagées entre le génie industriel et l’informatique. Ce document est structuré en deux parties : 1. la première partie permet d’exposer, dans mon Curriculum Vitae détaillé, un bilan de mes activités d’enseignant-chercheur. Mon parcours professionnel, mes activités d’enseignement et un bilan de mes activités de recherche sont exposés de manière synthétique. Dans un premier temps, les enseignements dont j’ai eu la responsabilité (conception et ou réalisation) ainsi que les documents pédagogiques produits et les volumes horaires sont exposés. Ensuite, les encadrements de chercheurs (doctorants, masters et post-doctorat), les projets institutionnels (FUI et ANR) dans lesquels j’ai pris des responsabilités, les partenariats avec des entreprises dans le cadre de contrats CIFRE, mes activités d’animation de la recherche au niveau national et international font partie de ce bilan. Cette section se termine par la liste exhaustive de mes publications et communications (section 3.5) réalisées depuis le début de mon activité de chercheur, en 1997, 2. la seconde partie synthétise mes activités de recherche réalisées depuis 2001. Cette seconde partie est présentée selon 6 chapitres. Le chapitre 1 permet d’exposer la problématique globale de mes travaux de recherche. Elle est orientée par un modèle à trois niveaux (Processus, Outils, Expériences / Connaissances) et étayée par un premier niveau d’étude bibliographique. Le niveau de détail choisi permet de comprendre cette problématique dans sa globalité. Les processus ciblés, les outils développés, les connaissances exploitées sont présentés au regard de la littérature dans les différents domaines. Les chapitres 2 à 5 fournissent quant à eux un niveau de détail plus fin. Ils permettent de présenter les problématiques de manière affinée, les développements réalisés et les contributions scientifiques majeures. L’objectif est de fournir des éléments qui soient utiles à la compréhension de mon activité de recherche mais, également, d’en favoriser l’exploitation ultérieure. Enfin, dans le chapitre 6, la conclusion permet de prendre le recul nécessaire au travaux réalisés et de proposer mon projet de recherche pour les années à venir