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International audience; End-of-life management of complex systems is increasingly important for industry because of growing environmental concerns and associated regulations. In many areas, lack of hindsight and significant statistical information restricts the efficiency of end-of-life management processes and additional expert knowledge is required. In this context and to promote the reuse of secondhand components, a methodology supported by risk assessment tools is proposed. The proposal consists of an approach to combine expert and statistical knowledge to improve risk assessment. The theory of belief functions provides a common framework to facilitate fusion of multisource knowledge, and a directed evidential network is used to compute a measure of the risk level. An additional indicator is proposed to determine the result quality. Finally, the approach is applied to a scenario in aircraft deconstruction. In order to support the scientific contribution , a software prototype has been developed and used to illustrate the processing of directed evidential networks. Index Terms-Belief functions, decision-support system, directed evidential networks, end-of-life management, risk assessment.

This article discusses 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.

International audience; no abstract

The problem of selection of project scenarios in a multi-objective context is difficult to solve in an optimal manner. The idea proposed in this paper consists in using previous experiences in order to accelerate the search process for solutions and to limit a too important combinatorial explosion. A framework which allows re-using knowledge resulting from experience feedback is proposed in this paper. This knowledge is capitalized from experiences about previously planned projects in order to guide and to refine the search for new solutions. The method for selection of scenarios is based on an evolutionary algorithm. This algorithm is modified in order to allow the reuse of capitalized knowledge. The knowledge is generated and updated starting from the results of the evaluation of the scenarios by the evolutionary algorithm and/or from the analysis of the projects previously carried out. They are gathered in an influence diagram (extension of the Bayesian networks for the decision-making) allowing their reuse by the algorithm.

This article aims at presenting preliminary results of a study carried out in partnership with Turbomeca Company about the problem engendered by the implementation of Experience Feedback (EF) processes within large scale companies. A model constituted of cooperating EF processes and adapted to hierarchical decision levels is proposed. Models of experiences corresponding to each level are defined and an illustration in concrete situation is described.

Lesson Learning is a process aiming to create knowledge through selection, analysis and reuse of past success and failures (experiences). This paper presents an approach to integrate a Lesson Learning System and a Risk Management Process in order to improve Project Management performance.

International audience; This article proposes a model and rules dealing with the management of the interaction between system design processes and project planning ones. An industrial benchmark analysis has reinforced our belief that the interaction between the two processes has to be supported by models, processes and relevant tools. Firstly, after presenting the results of the analysis, the different entities are defined and the one-to-one relationship or bijection between the structure of the system and the structure of the project is made. Then, a model, taking into account design activities and planning activities as well as management of interactions, is proposed in compliance with existing project and design standards. A process of interaction is presented to carry out design and project management. Two interaction modes have been proposed. On the one hand, the structural interaction establishes links between entities of the two domains. On the other hand, the behavioral interaction (subject of this paper) is based on the definition of states for each entity following feasibility and verification criteria, and can thus manage the changes between states. Some rules are defined (precedence and synchronous rules) to forbid certain changes when they are inconsistent and to synchronize them.

Cet article presente les premiers resultats d’une etude realisee en partenariat avec l’entreprise Turbomeca traitant des problemes engendres par l’implementation de processus de retour d’experience dans une entreprise etendue. La premiere partie de l’article est dediee a la definition et a la description des processus de retour d’experience et des approches les plus avancees pour faciliter leur implementation. Dans une seconde partie, nous montrons que dans une entreprise etendue, il est necessaire de definir de multiples processus de retour d’experience pour que l’approche soit adaptee aux differents niveaux de decisions et aux differents produits/technologies utilises dans l’entreprise. Nous proposons une trame generale pour integrer ces differents aspects et nous presentons une illustration d’un cas concret. Finalement, nous concluons sur l’originalite de notre proposition ses avantages et les perspectives de notre travail.

This article addresses the multi-objective optimization in system engineering project planning. At the earliest steps of a system engineering process, many choices can be done taking into account experience feedback. An approach that permits to a decision maker to perform the selection of scenarios during a project is presented. The objectives to optimize are the total cost of the project, its total duration and the global risk. An algorithm based on the Ant Colony Algorithm (ACO) and the Pareto front principles is used. It permits to explore the objective space and to propose to the decision makers Pareto-optimal solutions that are distributed on the Pareto front. The method permits to select solutions (different scenarios for a project) taking into account the risk that a problem arises. The ACO has been developed with the Ruby language and some experiments have been done in order to validate the approach.

Nowadays, one of the main difficulties of Production Management is to take into account the increasing uncertainty of the customer demand. In an MRP system, this uncertainty is mainly managed at middle term and through successive actualizations of the planning. We suggest in this paper a way to explicitly model the uncertainty and imprecision of the demand allowing to pass through all the MRPII steps (Material Requirement Planning, Load Planning, Scheduling). This method, named Fuzzy-MRP (F-MRP) allows to visualize at each step a much more rich information for the decision makers, taking into account not only the certain data but also a quantification of the various eventualities that may arise. Decisions requiring a long preparation (sub-contracting, order of components, increase of capacity, etc.) can so be considered earlier, on the base of quantified data.

In order to closely adapt what they produce to what will be sold, it is mandatory for nowadays companies to make early decisions on the base of an uncertain production planning. We suggest a way to explicitly model the uncertainty and imprecision of the demand using possibility theory, all along the MRPII steps: Material Requirement Planning, Load Planning, Scheduling. Though propagation of this uncertainty, the “Fuzzy-MRP” method aims at providing a richer information to decision makers, based on a quantification of the various situations that may arise according to the possible variations of the customer's demand.

Scheduling requires to set-up a number of parameters that have a direct influence on the schedule quality. Since scheduling is a highly unstable process, it is usually a long and complex task to tune manually these parameters in order to optimize a set of objectives. Meta-heuristics have recently been successfully used for schedule optimization, but an important modeling effort is usually required in order to express the problem to solve within the specific framework of each method. Moreover, these techniques are often time-consuming and their application to problems of industrial size may be hazardous. It is suggested in this article a way to combine meta-heuristics in a black box approach in order to select, then set-up scheduling parameters on industrial-scale scheduling problems, i.e. problems where several tens of criteria can be combined in order to build an objective function, several tens of parameters can be used, with a schedule involving several hundreds of machines and several thousands of tasks. An implementation framework has been developed and tested on an industrial scheduler, named Ortems®. The first results of the use of this framework on real industrial databases are described and commented.

Within the customer–subcontractor negotiation process, the first problem of the subcontractor is to provide the customer with a reliable order lead-time although his workload is partially uncertain. Actually, a part of the subcontractor workload is composed of orders under negotiation which can be either confirmed or cancelled. Fuzzy logic and possibility theory have widely been used in scheduling in order to represent the uncertainty or imprecision of processing times, but the existence of the manufacturing orders is not usually set into question. We suggest a method allowing to take into account the uncertainty of subcontracted orders. This method is consistent with list scheduling: as a consequence, it can be used in many classical schedulers. Its implementation in a scheduler prototype called TAPAS is described. In this article, we focus on the performance of validation tests which show the interest of the method.

Industrial information systems like Enterprise Resource Planning (ERP) systems are increasingly comprehensive and integrated. Nevertheless, satisfying all the user requirements regarding information processing or decision support within a unique tool seems still to be unrealistic. As a consequence, being able to quickly provide the users with additional pieces of software for supporting specific decisions remains more than ever a topic of interest. Specific developments take time, are costly, have usually low reliability and are often poorly integrated with the main information system. In order to address these drawbacks, we suggest a structure and the first elements of a toolbox aimed at allowing an easier development of additional pieces of information/decision support system (DSS) by reuse of standard software components. This toolbox allows the implementation of workflow and groupware facilities and the communication between modules is achieved through a database which provides the integration with the main information system. The first decision support modules developed include an expert system generator, a neural network simulator, a simplex module and a Case-Based Reasoning (CBR) module. Examples of applications developed using this toolbox are described, and a development methodology is suggested.

Part 1: Knowledge Discovery and Sharing; International audience; This article presents the first results of a study which deals with the detection and the correction of abnormal values in data series intended to forecast demand. This work fits in the broader context of performance management for proximity retailers. Indeed, when this kind of point of sales (POS) is studied, sales volumes are often too small to be effectively exploited by statistical processing methods. It is therefore useful to consolidate the information with expertise and additional knowledge resulting from similar POS. It is also relevant to take into account the inherent uncertainty of such information. The proposal of this paper is a methodological contribution which uses consolidated knowledge to detect and correct abnormal values and to improve the quality of data used to implement forecast methods.

This paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

The study of Smart Sensors/Actuators led, during the past few years, to the development of facilities which improve traditional Sensors/Actuators in a necessary way to automate production systems. In an other context, many studies were carried out aiming at defining a decisional structure for production activity control and the required reactivity led to the autonomisation of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture enabling the exploitation of Distributed Smart Sensors/Actuators in the decisional system dealing with machine tool monitoring and machining.

L'etude des Capteurs/Actionneurs Intelligents a permis, au cours de ces dernieres annees, le developpement d'entites enrichissant les Capteurs/Actionneurs traditionnels de fonctionnalites originales et indispensables a l'automatisation des moyens de production. Dans un autre contexte, de nombreux travaux ont porte sur la structure decisionnelle qui permet de piloter les systemes de production et le souci de reactivite aux perturbations a conduit a autonomiser les niveaux decisionnels les plus proches du systeme operationnel. Nous proposons dans cet article d'etudier la convergence naturelle qui existe entre ces deux approches et nous suggerons une architecture d'integration permettant l'exploitation de Capteurs/Actionneurs Intelligents Distribues dans le systeme decisionnel.

The operational levels of production management, often called production activity control (PAC) or manufacturing process control, require increasing reaction capabilities in order to adapt the workshop management to the changes of its environment. It often implies giving more responsibilities to the low decision levels. However, the management of the corresponding degrees of freedom is generally unusual. In such a situation, decision support systems (DSSs) provide a way to reconcile the satisfaction of mid-level objectives and the reaction requirements. A conceptual model is described that provides a design framework for a PAC DSS. Since the available knowledge lies mainly in expertise, a DSS has been implemented using a knowledge-based system. The uncertainty and imprecision of the managed information led to the use of fuzzy logic as a modeling tool. Moreover, various inference semantics have been implemented in the expert rules because different kinds of reasoning have been identified. Two versions of the DSS are described and several examples of implemented reasoning processes are developed.

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ‘‘Rex Manager’’ with principles of sustainable management for continuous improvement of industrial processes in companies.

This paper aims to present a study on knowledge management. We develop decision-support mechanisms hybridizing statistical and cognitive experience feedback to perform risk assessments on critical areas of a system. We propose an approach for combining expert opinion and statistics by using belief functions and by processing the combined knowledge in a directed evidential network to provide a risk measure.

International audience; The goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals.

Most of the available industrial schedulers are based on a simulation approach using dispatching rules. These rules are often dedicated to the satisfaction of a single performance criterion, and are used whatever the characteristics of the workshop or of the set of jobs. An approach which allows one to bring in compromises between rules is set out in this paper. These compromises can be parametered in accordance with the objectives of the workshop and the characteristics of the jobs in order to introduce some reactivity in the decision system. Three ways to set up the parameters are compared: experimental design, fuzzy expert system and neural network. The method allowing one to define compromises can be implemented on each scheduler that uses a simulation approach. Tests have been made with an industrial scheduler called SIPAPLUS, the results of which are developed in this paper.

Job-shop scheduling through simulation uses various kinds of dispatching rules such as SPT or the slack time rule. Each of these rules aims at satisfying a single criterion although workshop management is a multi-criteria problem. This paper proposes a way to use fuzzy logic in order to build aggregated rules allowing to obtain a compromise between the satisfaction of several criteria. When the criteria of performance change with the evolution of the production environment, these aggregated rules can be parametrized in order to modify the respective influence of the elementary rules they are composed of.

Production Activity Control, considered as the operational levels of production management, requires quick decision making in order to react to changes on the resources or on the workshop environment. The design of a Decision Support System in such a context implies to model the reasoning of the experts of the field. A simple conceptual model of Production Activity Control is suggested, in order to identify the types of reasoning used. The uncertainty and imprecision of the information led us to use fuzzy logic in order to select various inference semantics, adapted to the types of decision identified. Examples of implemented reasonings are described.

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.

International audience; This article proposes a model dealing with the synchronisation of system design process and project planning process. This synchronisation is named synchronous coupling and is based on the assumption which states that a mapping exists between system Bill Of Materials and projects Work Breakdown Structure. Synchronous coupling ensures a consistency in the monitoring and the control of the system design and project planning by imposing synchronous milestones during the project development. The synchronous coupling is based on the definition of three states for each design and project entity following feasibility and verification criteria, and can thus manage the changes between states. Some rules are defined (precedence and coupling rules) to forbid certain changes when they are inconsistent and to synchronize them.

Taking into account the experience feedback on complex problems solving in industrial organizations is a way to improve the quality of products and processes. However, few academic works deal with representation and instrumentation of experience feedback systems. We propose in this paper a model of experiences and mechanisms to use these experiences. More specifically, we wish to promote the reuse of expert analysis already performed to propose an a priori analysis to address a new problem. The proposed approach is based on a representation of the context of the experience using conceptual markers and a conceptual representation of the analysis explicitly incorporating expert opinions and their combination.

National audience

9th International Conference on Evolution Artificial, Strasbourg, FRANCE, OCT 26-28, 2009; International audience; Several recent works have examined the effectiveness of using knowledge models to guide search algorithms in high dimensional spaces. It seems that it may be a promising way to tackle some difficult problem. The aim of such methods is to reach good solutions using simultaneously evolutionary search and knowledge guidance. The idea proposed in this paper is to use a bayesian network in order to store and apply the knowledge model and, as a consequence, to accelerate the search process. A traditional evolutionary algorithm is modified in order to allow the reuse of the capitalized knowledge. The approach has been applied to a problem of selection of project scenarios in a multi-objective context. A preliminary version of this method was presented at EA' 07 conference [1]. An experimentation platform has been developed to validate the approach and to study different modes of knowledge injection. The obtained experimental results are presented.

International audience; 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.

1st International Conference on Complex System Design and Management (csdm 2010), Cite Int Univ Paris, Paris, FRANCE, OCT 27-29, 2010; International audience; This paper proposes first integrated models dealing with the management of the coupling between system design environment and project planning one. A benchmark done 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. An integrated model taking into account design and planning requirements as well as management of coupling is proposed in compliance with existing project and design standards. A process of coupling, carrying out design and project management in case of innovative design is presented. It is based on the generic formalization of the interactions and the propagation of decisions taken within an environment to another one.

International audience

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving the missions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable.

Intelligent optimization is a domain of evolutionary computation thatemerges since a few years. All the methods within this discipline are based onmechanisms for maintaining a set of individuals and, separately, a space ofknowledge linked to the individuals. The aim is to make the individuals evolveto reach better solutions generation after generation using the knowledge linkedto them. The idea proposed in this paper consists in using previous experiencesin order to build the knowledge referential and then accelerate the searchprocess. A method which allows reusing knowledge gained from experiencefeedback is proposed. This approach has been applied to the problem ofselection of project scenario in a multi-objective context. An evolutionaryalgorithm has been modified in order to allow the reuse of capitalizedknowledge. This knowledge is gathered in an influence diagram allowing itsreuse by the algorithm.

International audience

Business integration requires that each partner can guarantee not only the quality of its products, but also the qualification and competence of its workforce. Usual models, like those included in the Human Resource Management modules of Enterprise Resource Planning (ERP) systems, are not sufficient in highly constrained domains like aeronautics. We show in this chapter how a generic competence management model has had to be modified and enlarged to satisfy such constraints. On the base of this model, software has been developed that is currently being implemented in three sites belonging to two companies. We shall show how such software may allow, on one hand, to guarantee that only competent people have been involved in the various steps of the manufacturing process, but also to improve the way operational competences are managed in the company.

This paper studies the co-design paradigm which refers to the concurrent design of products, their associated services, internal processes and networks of partners. The co-design is a powerful strategic positioning technique that will give opportunities to firms to reach and maintain a profitable position on the market. This means that future co-working needs should be taken into account from the beginning of the product life cycle. In this paper, we define some of the basic co-design concepts and also provide a set of indicators that allow the qualification of the collaboration of partners. These indicators are illustrated on a car assembly example.

The implementation of an integrated information system, like an ERP (Enterprise Resource Planning) or an APS (Advanced Planning Systems) is nowadays a key issue for companies. The problems that can appear during their integration, which is always a difficult task, are nowadays better identified even if their origins are often multiples and complexes. Defining new business processes and coupling systems with users are thus, sources of many problems. The integration of such systems requires a mapping between the processes used in the company and the standard processes supported by the information systems. In that purpose, a BPR (Business Process Reengineering) project always precedes the implementation of the information system. We suggest to better adapt the business processes to the human actors by explicitly taking into account concepts like the role, competence and knowledge of the human resource. We show how these concepts may complement techniques like BPR in order to better identify the needs and possibilities of the workforce, with the final goal of increasing the efficiency and acceptability of the system to be implemented.

The capitalisation of the know-how and experiences becomes a major issue of the industrial world, especially in large companies. Lesson learned techniques and experience capitalisation are possible methods for allowing the companies to increase their knowledge on their internal processes. This paper aims at presenting a study carried out with Alstom Transport on the “Experience Feedback” and “Lesson Learned” problems. We show how an Experience Feedback (EF) process, mainly aiming at transforming data into information, then information into knowledge, can benefit from an explicit modelling of concepts like role, competence and knowledge of the actors. We also show how these concepts may help to better identify the needs and potentialities of the actors, with a twofold goal: increasing the efficiency and acceptability of the EF system to be implemented on one hand, and improving the implication of the human resources in the technical processes on the other hand.

Nowadays, the development of high quality and high performance products, with short time to market, represents a challenge for manufacturers. So, they must take into account, in the early steps of the product development, constraints that usually appear during the project management process. In this article, we define a process, based on the reuse of technical and nontechnical knowledge and experience feedback, that improves the communication between the design and the project management teams, in order to facilitate the emergence of collective solutions. After a brief presentation of the research objectives, this paper tackles the problem to closely connect design and management and suggests a methodology to associate the project tasks with the functionalities that the product has to implement.

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system.

For a long time, workshop managers have only expected from industrial schedulers to find quickly and sometimes interactively a feasible schedule. However, since the schedule governs the manufacturing resource usage, companies now require to optimize their schedule according to more or less complex criteria. This optimization is not only necessary in order to allow the company to remain competitive, but also in order to allow it to efficiently participate to Supply Chains in a Collaborative context. Many parameters are present in nowadays industrial schedulers which have a direct influence on the schedule quality. Tuning these parameters is a long and complex task if performed manually, which can be addressed by combinatorial optimization methods. Meta-heuristics have also been successfully used on specific scheduling problems, but the size of industrial problems and the necessity to provide a generic solving framework set specific difficulties. We show in this communication how several meta-heuristics can be integrated and combined in an environment allowing both to define a complex objective function, on the base of several elemental criteria, and to optimize the schedule by finding the best tuning of several tens of scheduling parameters, Typical results based on the cases of real workshops are provided.

In the present study, we suggest an approach of interactive scheduling based on the definition of various types of indicators. The purpose of these indicators is to help a workshop manager to assess the performance of a schedule but also to identify the best way for improving it.

The production centre Alstom Transport in Tarbes, France, designs, industrialises and ensures the maintenance of several high technology equipment (such as power modules, command platforms...) of the traction part of trains, metros or tramways. A study has been launched on the problem of defining an architecture for the Return on Experience (RoE) and its integration into operational industrial processes such as design, integration or validation. The first part of this paper shows several ways that have been developed in several firms and organisations in order to handle the RoE and proposes a synthesis of their key elements. The second part aims at defining an alimentation process for the RoE and the third part proposes several considered functions for the exploitation part of the RoE.

Uncertainty has always been inherent to forecasting activities, but is usually implicitly considered. When taken into account, it is managed on a long time horizon, at the highest level of the planning process. Nevertheless, uncertainty on the occurrence of orders may nowadays also be present in short term planning and scheduling, especially for sub‐contractors or for companies that manufacture short life products. A method allowing to take into account the uncertainty on the occurrence of the orders in scheduling has been suggested in [1]. In order to validate the method, a range of tests has been performed and is described here. Their purpose is to tune the different overbooking parameters in order to obtain a production planning forecast under uncertainty that can be as close as possible to the final schedule composed of firm orders.