The COSTEAM project deals with optimal and secure management of discrete systems producing goods and services. Its main objectives are systems design, analysis and control.
Systems producing goods or services play a fundamental role in our economical environment which is facing major changes. In industrial production, the buzzword is no more productivity but capacity to react or agility. The preservation or the improvement of the competitiveness of an industrial or service system is strongly influenced by its environment, i.e. by the evolution of the market, by production technologies and by the people involved in its operations. Concerning the tomorrow's market, the economic studies are unanimous. The market evolves towards high-quality, low-cost products, of large variety (some even talk about personalized products or mass customization) and renewed more frequently. It constantly evolves and becomes more and more difficult to predict. In such a very strongly competitive context, the performance of companies depends on their flexibility and their ability to react. The technological answer to this challenge relies on flexible and reconfigurable systems. A flexible manufacturing system has a high level of automation and uses sophisticated resources such as digitally operated machines, robots, etc. The amount of investments due to flexible systems requires a detailed preliminary study. Such a study requires a design phase comprising stages for modeling, analysis, performance evaluation and control synthesis of the future systems, even before these are settled. Their reconfiguration uses the same techniques to improve or adapt constantly these systems to the needs of the market. Due to the globalization of economies, companies must be competitive in terms of costs, delays and quality. They must be flexible to meet the fluctuating demands and they must be reactive to face the exogenous and endogenous changes they undergo. More than ever they need rational tools and structured methods to better control the flows of products, information and decision, and to archive a better reliability of the production resources. The control of the whole supply chain, going from the suppliers to the consumers and encompassing all the stages of production, becomes a necessity. The objective of the COSTEAM project is to develop tools and methods which can help companies to design, model, evaluate and manage their production systems. Accurately, starting from industrial needs, we develop methods for modeling and evaluating performances, as well as associated tools and methodologies to help manufacturers to better design and manage their production systems of goods and services.
Project COSTEAM addresses three complementary themes:
Theme 1: Performance evaluation and systems sizing
The aim of this theme is to evaluate systems producing goods and services in order to design/re-design them or to provide them with efficient management and development strategies. Both generic optimization methods dedicated to common systems and analytical and simulation methods dedicated to more particular systems are addressed in this theme.
Theme 2: Optimal control of systems
This theme deals with designing of controllers for discrete event systems. The control synthesis of discrete event systems including time constraints and the uncontrollable/unobservable nature of some events is addressed. We pay special attention to the notions of deadlock avoidance and recoverable systems for the case of industrial systems. The aim is to build the less restrictive control law which guarantees that the behavior of the system respects given specifications.
Theme 3: Reliability and maintenance systems
This theme deals with the development of efficient maintenance policies in the context of the production of goods or services under strong constraints. The proposed policies are developed by taking into account the production plan and the maintenance plan of the manufacturing system. The main challenge of these topics is the integration of modeling and optimization techniques within a unified analysis process to design and to manage a complex system and then to synthesize its control. We give special attention to modeling of the maintenance activities, starting from the design phase of the manufacturing system.
These three presented thematic intend to provide systems producing goods and services, and more generally enterprise networks, with optimized strategies of control and development. For our case of studies, we do not consider isolated entities, but we take into account the relationships existing between the various partners. Two domains of application will be studied:
Industrial systems and services systems(such as hospitals). We consider an entity that produces goods or services, taking into account complex constraints as well as interactions with its customers and its suppliers. We propose to develop both analytical methods and generic methods to optimize the behavior of such systems.
Enterprise networks. On the basis of models developed in the MACSI project, for this kind of application we study the operational management of the whole supply chain with respect to the organization, the monitoring and the optimization of this supply chain. We also propose to consider e-procurement business subject to a certain amount of constraints within the framework of open markets via new technologies of information and communication.
The performance evaluation and systems sizing aspect is very important for both application domains. Indeed, the research in this field is connected to the developments of discrete event systems. It is often caricatured by the rivalry between analytical methods and simulation. Some recent results allow to get rid of this rivalry and to make analytical methods and simulation complementary. Thus we develop analytical methods on the basis of simulation to evaluate and optimize systems producing goods and services. A new dimension in this research is the simultaneous consideration of the imperatives of production and the maintenance policies as well as the implementation of relevant indicators. Other research topics such as the study of hybrid systems (Discrete Event Systems and Continuous Systems) could be mentioned. In order to avoid dispersion of our research energy, these systems will not be approached on a short term but may be studied if the opportunity appears (i.e. industrial contracts) and if the human resource potential of the project allows.
COSTEAM project falls under the INRIA topic entitled "Numerical systems - Control and complex systems". It aims at proposing solutions and participating in knowledge advancement related to design, evaluation and management of discrete systems producing goods or services. The research goal is resolutely dual, giving top priority to fundamental research on one hand but always keeping in mind industrial applications on the other hand. On the short-term, we will thus focus on well-identified problems of design, performance evaluation, systems optimization, maintenance policies definition, and also optimization of e-procurement. Thanks to this experience, the aim on the long-term (5 years and more) is to develop a systematic method to design and analyze systems producing goods or services based on modeling and formal specifications of the structure and control of these systems, like software engineering does. The COSTEAM project takes benefits from the activities of the "Systémes de production (SdP)" team of the "Laboratoire de Génie Industriel et Production Mécanique" (LGIPM), common to ENIM, ENSAM-Metz and University Paul Verlaine of Metz. Most of its members belonged to the former MACSI project of INRIA.
The COSTEAM project is built on concrete industrial needs which concern problems of design, management and optimization of systems producing goods or services. Our research activity relies on conventional tools like operational research, Petri nets, perturbation analysis and others for solving problems encountered in designing and managing systems producing goods and services. These tools are the foundation for developing new methodologies more adapted to the industrial applications and the research challenges we are facing.
For a long time, only systems producing goods were studied with a lot of applications in the manufacturing world. More recently, systems producing services have been considered. These kinds of systems include government services, banks, health services and hospitals, maintenance services, large distribution channels, etc. A common feature to these fields is their strong socio-technical component, the role of human beings remaining the driving force of these systems, throughout many functions (actor, decision-maker, operator, customer, etc). Thus it is very important to consider also these systems, by integrating new constraints (and more particularly the social, economical, technological and environmental ones), together with the complexity of the proposed systems with their human dimension. Therefore, the COSTEAM project tackles the study of critical problems of optimization and decision-making existing in logistic systems, production systems and services systems. The goal is to evaluate, design and manage the following kind of systems:
Manufacturing systems such as:
Discrete systems, existing for instance in the automotive industry, aeronautics or mechanical production;
High-speed systems (with high rate flow of parts) or current flow systems that are very common in companies from various industrial sectors (e.g. food industry, pharmaceutics, cosmetics, electronics).
Services systems(government services, banks, health services, maintenance services, etc). The problems encountered are diversified. For instance, they can concern the definition of timetables; the determination of maintenance plans by maintenance agents.
Logistic systems(supplying, production, distribution and transport), under their strategic, tactical and operational aspects. More specially, we are interested in:
The logistical and industrial strategy and the problems related to location and sizing of logistic and industrial units;
The scheduling and optimization of supplies, stocks and distribution;
The scheduling and optimization of load transport, and more particularly long-distance transport, combined transport and vehicles rounds, supplying transport, inter-factories transport and distribution.
Among all these domains, a new application field addressed in our team is the E-sourcing, which is a network of electronic management purchasing. Indeed, the emergence of new technologies provides manufacturing companies with new solutions to increase their competitiveness. E-sourcing represents an effort from companies in their use of Internet to optimize their purchases using electronic trade, on-line services for calls for proposals, virtual market places, etc.
The Internet and the Web services allow companies to get over the geographical limits, to take benefits of a worldwide market and to build new relationships between customers and suppliers. A well-managed project based on E-sourcing is a major source of profits and reduction of administrative expenses.
However these technologies also bring new risks and weaken companies exploiting the worldwide market. This is the reason why one of the key factors for success is the capacity of companies to integrate an unstable relational framework, oscillating according to the object and the moment.
Studying the customers-suppliers relationships in the context of the e-sourcing, integrating the E-sourcing into the optimization of a purchasing program taking into account risk factors (economical, social and environmental ones) are the main objectives of our research in this application field.
This part concerns the performance evaluation of critical problems. Setting up a new production system of goods or services, or modifying either the physical structure or the operation of an existing system, requires the system's performance to be evaluated and optimized.
The first general subject concerns the scheduling of flexible systems (production or services) without storage capacity and with a specific blocking constraint encountered in many industrial processes. In classical blocking situations, a machine remains blocked by a job until this job starts the operation on the next machine in the routing. For the particular type of blocking constraints considered in our work, the machine remains blocked by a job until its operation on the downstream machine is finished (RCb constraint). Another new type of blocking constraint has been proposed and defined . Heuristics and genetic algorithms for solving these Flow-shop problems are developed and different initial populations are tested to find the best adapted . A mixed-integer linear programming model and a lower bound. has been proposed to the hybrid Flow-Shop case .The metaheuristic called electromagnetism-like optimization heuristic (EM) developed to minimize the makespan of Flow-shop has been adapted to the hybrid Flow-Shop case . To solve the Job-Shop scheduling problem, possible conflicts situations are characterized and a heuristic method which avoids conflicting situations has been proposed .
The impact of delays such as transportation, production or lead-times, is also studied in another aspect of our research program. The basic idea of this study is to develop Perturbation Analysis (PA) for performance evaluation and optimization of failure-prone manufacturing systems. Indeed, in the domain of discrete event systems, it was discovered in the early 1980s that event-driven dynamics give rise to state trajectories (sample paths) from which one can very efficiently and nonintrusively extract sensitivities of various performance metrics with respect to at least certain types of design or control parameters. This has led to the development of a theory for perturbation analysis in discrete event systems. Using PA, one obtains unbiased gradient estimates of performance metrics that can be incorporated into standard gradient-based algorithms for performance evaluation and optimization purposes.
Systems with delays hardly have begun to be investigated, and the few existing results indicate that the problem may become challenging, and the PA derivatives are more complicated than those that would be obtained for the system without delay. In our work we consider two models: discrete and continuous flow models, with inclusion of delivery times for more realistic performance evaluation and optimization of failure-prone manufacturing systems. While in most traditional continuous flow models the flow rates involved are treated as fixed parameters, a continuous flow model has the extra feature of treating flow rates as stochastic processes. Furthermore, continuous models have been shown to be very useful in simulating various kinds of high volume manufacturing systems and in this case are a good approximation of discrete settings. However, when the study should consider each part independently, discrete flow models are more appropriate. Unfortunately, PA estimates could become in this case biased (hence unreliable for control purposes) when significant discontinuities in sample functions appear.
One salient feature of our work is the explicit modeling of delays without destroying the nature of pure continuous and discrete flow models. Thus, the main innovation of our research is to consider these both models with delays and to define PA estimates for performance evaluation and optimization. In a first step, discrete flow model for a simple manufacturing system composed by a failure-prone machine, a buffer and a customer is considered. The demand is stochastic and the delivery time between the buffer and the customer is supposed to be constant. The control policy applied to the machine is a hedging point policy which has been proved to be optimal for a system without delivery times and which ensures that the material does not exceed a given number of parts. A simulation based on perturbation analysis is then proposed for performance evaluation and optimization. The goal of the optimization is to evaluate the optimal buffer level (hedging point) to minimize the total cost function which is the sum of inventory cost, backlog cost, transportation cost , . The PA estimates are shown to be unbiased and comparison with discrete event system simulation proves that our results are very interesting and performing. This problem is then extended to a system with stochastic delivery times with similar results . In a second step of our work, the study of both continuous and discrete flow models with constant delivery times is pursued. By theoretical and numerical results it is shown that these models have the same behavior and for each model unbiased gradient estimates are obtained .
Another research domain we considered deals with the optimization of production - distribution systems. We considered a multi-stage production-distribution system made up of production plants separated by warehouses. Customer orders arrive randomly to the finished goods warehouse according to a compound Poisson process. The quantity of each order is a random non negative integer variable and the quantities of different orders are iid random variables. First of all we developed an analytical approach to solve the optimization problem for a production-distribution system with two levels . However, as the size of the system increased, building an accurate analytical model became very difficult. Consequently, we proposed a simulation based optimization method to optimize the system when both the performance function and some constraints are evaluated by stochastic discrete event simulation . We showed that, under some mild assumptions, the algorithm converge to a local optimum with probability 1.
Reconfigurable manufacturing systems (RMS) have been acknowledged as a promising means of providing manufacturing companies with the required production capacities and capabilities. This is accomplished through reconfiguring the system elements over the time for a diverse set of individualized products often required in small quantities and with short delivery lead time. In , we focused on the various enhanced features of the RMS when compared to the existing manufacturing systems and identified the need for the changeover. The various requirements of this kind of manufacturing structure are identified. Further the problems and the research gaps with the implementation are listed and possible steps to be taken for the successful implementation of RMS in practice are presented.
Moreover, to map the manufacturing system capabilities and other characteristics, RMS necessitate the developpment of a suitable index. As a new result, we developed an index to measure the reconfigurability of RMSs keeping in mind their various core characteristics such as modularity, scalability, convertibility and diagnosability. These characteristics are mapped together using Multi-Attribute Utility Theory (MAUT). We can easily use this index to find the reconfigurability of a system possessing different characteristics .
This subject concerns supply chains. We begin by the more classic logistic systems, and then move on to enterprise networks which are characterized by an extensive use of information technology.
Integrated supply chains are complex systems and their modeling, analysis and optimization requires carefully defined approaches/methodologies. Also, the complexities may vary greatly from industry to industry and from enterprise to enterprise. In contrast to traditional integrated supply chains, integrated long supply chains are more complex, with many parallel physical, information and financial flows occurring in order to ensure that products and/or services are delivered in the right quantities, with the requested quality to the right place in a cost effective manner at the right time. There is no generally accepted method by researchers and practitioners for designing, operating and evaluating agile integrated long supply chains. Therefore, our research work has attempted to investigate technologies, systems and paradigms for the effective management of long integrated agile supply chains. More specifically, a vision of the future technical issues and an insight into the future scientific and industrial advances required to meet future market and public demands are addressed. Two developed approaches for modeling and evaluating agility in integrated long supply chains respectively Fuzzy Intelligent based approachand Fuzzy association rules mining based approachare developed .
Supplier selection with order splitting represents one of the most important functions to be performed by the purchasing department that determines the long-term viability of dynamic supply chains. As a second result, a novel approach for automatic knowledge acquisition, which clubs supplier selection process with order splitting for dynamic supply chains based on the attained knowledge from the variations in the market is developed. Moreover, the suggested approach imitates the knowledge acquisition and manipulation in a manner similar to the human schedulers who have gathered considerable knowledge and expertise in a given domain. As per this concept, those decision criteria for supplier selection are considered first, which are qualitatively meaningful like performance, service quality, innovation, risk etc. and thereafter their application is quantitatively evaluated. State variables are derived from the decision criteria to match the factors (flexibility, responsiveness, agility, position etc) associated with local competitive situation of the candidate supplier. Therefore, logically it can be inferred that the developed approach can generate decision making knowledge as a result, the developed combination of rules for supplier selection can easily be interpreted, adopted and at the same time if necessary, modified by supply chain decision makers , .
Moreover, another research direction addresses the integrated facility location and supplier selection for design of a stochastic distribution network with unreliable suppliers. The network is composed of a set of suppliers serving a set of retailers through a set of Distribution Centers (DCs) to locate. Each retailer faces a random demand of a single commodity, the supply lead-time from each supplier to each DC is random, and no supply lead-time between DCs and retailers are considered. Firstly, we considered the facility location/supplier selection problem where all the suppliers are reliable. The problem concerns the selection of suppliers, the location of DCs, the allocation of suppliers to DCs, and retailers to DCs, where the goal is to minimize inventory and safety stock costs at the DCs, ordering costs and transportation costs across the network, and fixed DCs location costs. Secondly, we proposed a two-period decision model in which selected suppliers are available in the first period and can fail in the second period. The facility location/supplier reliability problem is formulated as a non-linear stochastic programming problem. A Monte Carlo optimization approach combining the sample average approximation (SAA) scheme and a Lagrangian relaxation based approach is proposed .
The globalization of economy and the exchanges give birth to multi-site companies who own their own production centers and distributions centers, which distribute on great geographical areas. Since always, the distribution of products with various modes of transportation is not taken into account in the management of supply chain. On the contrary, it is the external service provider of the supply chain who always manages it, but it does not support the measurement of the performance to control the cost. The discounted growth of the goods carriage per mode in European Union would encourage the decision makers to take measures to limit their use of it and especially to limit their environmental impacts. Actually, in an era with more environmental conscience on a global level (Kyoto, Goteborg, Copenhagen, etc.), the companies and service providers could no longer reject indefinitely on the community of environmental costs and will be, in all probability, subjected to heavy environmental tax in next years. The integration of the environmental and societal cost of transportation , , in the supply chain is rarely quoted in the literature. This activity justifies the integration of the constraint in the model by the current state of environmental situation, the evolution of the legislation opposition to the problems generated by pollution (EURO 5 for European Union, for example), and the public pressure which is increasingly attentive with the environmental problems and the actions for reducing the pollution. We have also adapted multi-criteria methods AHP or ELECTRE to our model , , .
Another research area for our team concerns the traceability phenomenon implementation within the production organization, particularly in the field of raw materials management in the food industry. The objective is to minimize the raw material's dispersion in the manufactured products. We seek to solve the problem of raw materials allocation into finished products, in order to minimize its dispersion and moreover, the products recall if needed. Dispersion optimization is made using a genetic algorithm . The dispersion criteria are afterwards used to determine production's criticality in terms of sanitary risk, from which it is possible to optimize the processes of picking and dispatching. The final objective is to reduce the number of recalls in case of a crisis. This is achieved by using the decision-making aid, operational research and artificial intelligence tools and .
The research work that our team is doing in this domain deals with discrete event systems modeled by Petri nets. Specifications considered are modeled by General Mutual Exclusion Constraints. In the past out team has developed control synthesis techniques which based on the theory of regions. These techniques provide efficient controllers for discrete event systems modeled by bounded Petri nets. Furthermore, we proposed an efficient control synthesis algorithm for marked graphs not necessarily bounded. However, this approach does not take into account the liveness of the closed loop system.
This year we continued our research work on developing a deadlock free controller for discrete event systems modeled by live marked graphs subject to General Mutual Exclusion Constraints. We show that the restriction imposed by controller may generate deadlocks even if the marked graph to be controlled is live. Thus, we prove that the risk of deadlock is a consequence of the existence of a particular structure that we call risky transitions. Furthermore, we proposed a sufficient condition to avoid deadlocks and developed a suboptimal deadlock free control synthesis method for marked graphs not necessarily bounded . Then we improved this technique by providing a necessary and sufficient condition to avoid deadlocks and we proposed a maximal permissive deadlock free controller for the same class of control problems . However, the occurrence of some events can not be observed during the evolution of the system. Therefore, the next step of our research work is to integrate the events observability and propose a deadlock free control synthesis for partially observable marked graphs.
Some control synthesis problems are subject to specifications which consist in avoiding given values for the marking of Petri net places. In order to handle these problems, we propose a new type of specifications called Marking Exclusion Constraint (MEC). The main advantage of MEC specification is an increased modeling power regarding General Mutual Exclusion Constraints (GMEC). We define two types of MEC: MEC-OR and MEC-AND and we propose a technique to build the controller which enforces MEC specifications for discrete events systems modeled by marked graphs .
The principle of control synthesis is to authorize or forbid the occurrence of controllable events according to the state of the system in order to prevent the evolution of the system to states which are not desirable. Time information on the occurrence of the events can be used to compute more permissive control laws as the controller does no longer need to completely forbid the execution of an event. Time introduces a new dimension of considerable interest in DES control, but also of significant complexity. Actually our research work deals deadlock free control synthesis for timed marked graphs subject to GMEC and MEC specifications.
Another research direction deals with building optimal control laws which aim to optimize given performance criteria while respecting specifications on resource availability and security. The applications concerned by this research activity are the air traffic control systems. The proposed approach uses time Petri net modeling tool to represent an air traffic management system. Then we built the time reachability graph and we associate a given cost to each state. The cost function takes into account the waiting time before take off, the cost of flight canceling and the cost of carburant burned by the airplane during a flight. We also take into account the perturbations in the capacity of airways which may vary according to climatic conditions. To overcome these perturbations, different flying scenarios are generated which include: 1) delay the flight; 2) using other airways and 3) cancel the flight. The approach that we proposed allows computing the optimal flying scenario for an air traffic system made of two airports and several airways with variable capacity . Actually, we are concerned with extending this problem to air traffic management systems with multiple airports and dynamic resources allocation.
Generally, this theme concerns the optimization of integrated maintenance policies for manufacturing systems. New integrated maintenance policies are developed and optimized in order to prove its performance according to the traditional policies existing.
Nowadays, the hard competition between the enterprises brings us to revise the currently adopted strategies of production and maintenance. In fact, the satisfaction of the client in time became a difficult spot since demands are random. In this context, subcontracting is defined as the procurement of products or services from external sources. It is justified by many reasons like cost reduction, production flexibility improvement, skill/resource shortage or proximity to markets. In this context our works deal with problems of management of subcontracting services for subcontractor enterprises. We study the constraint of subcontracting under a combined approach of maintenance management and production control for production system under a supplier - customer relationship with a "principal costumer". In order to increase the exploitation of the production capacity this system provides subcontracting services to another customer called "contractor", under a subcontractor - contractor relationship. In , we considered the profitability of subcontracting activities for subcontractor companies. The subcontracting imposes periods of unavailability of the production unit in order to periodically perform subcontracting tasks. We analytically show the conditions under which subcontracting is profitable, based on a given policies of maintenance, production control and assignment to subcontracting. We discuss profitability of two cases of subcontracting constraints: occasional and long-term relationship. In the first case we integrate the subcontracting task in the subcontractor plan without changing the decision variables values (maintenance interval and stock level). The second case imposes a new optimization of decision variables. For this case, we investigated the problem of the unforeseen extension of the subcontracting duration and its impact on the generated costs of maintenance, inventory and shortage. In the same context, we study analytically the importance of the beginning instant of subcontracting tasks in , . This study proves that the optimal instant to allocate machine to subcontracting is exactly the moment when the capacity of the stock is reached.
Dealing with the development of new integrated maintenance policies under the subcontracting constraint, another subcontractor aspect is studied. We started by considering two subcontractors which have different service cost and availability rate. The strategy consists at relaying on one of the two subcontractors and switching to the other at certain dates. This strategy is justified and optimized analytically in order to determine the optimal preventive maintenance date for the manufacturing system and the optimal switching dates between the two subcontractors . Furthermore, we proposed a prospect related to this study. The goal is the development of an optimal switching strategy between several subcontractors. We considered the building of a safety stock contrarily to the just in time strategy.
Considering only one subcontractor which satisfies a part of the demand in order to take the possibility of building a safety stock, a new research direction is explored. The objective is to manage simultaneously the integrated maintenance-production plan by determining the optimal safety stock level and the optimal preventive maintenance dates while minimizing the average total costs (production, maintenance, inventory and demand loss) .
More then, it's easy to see that the manufacturing system degradation evolve according to the production rate, Thus, for a given randomly demand, we established an optimal production plan which minimizes the average total holding and production costs. Using this optimal production plan and its influence on the manufacturing system failure rate, an optimal maintenance scheduling which minimizes the average maintenance cost has been established . We have solved analytically the problem using the linear quadratic Gaussian (LQG) control theory .
Another study deals with the combination between production and maintenance plan for a manufacturing system satisfying a random demand subjected to random failures of the manufacturing system. The aim of this study is to establish an economical production/maintenance plan minimizing the average total cost and to illustrate the significant influence of the production rate on the manufacturing system degradation . Moreover, considered an extended version of the problem by taking into account the demand rejection . Two studies investigating new intelligent integrated maintenance and production or service strategies, dealing with complex reliability problems are presented in .
In the same context of the dependence between production and failure rates, we considered the problem of production control when production rates depend on the failure rate. The objective is to determine the production planning over a finite horizon minimizing the generated costs (inventory, production and maintenance) .
Another research work uses the prognosis concept to develop a set of maintenance policies which integrate the schedule of maintenance missions performed by navy ship . The prognosis result is based on the evaluation of the degradation law, i.e. by taking into account the variations of the environmental and operational conditions. The aim is to determine the optimal business plan (choice and scheduling missions) combined to an optimal maintenance plan. To model the failure law, we established a relationship between the times between failure (from feedback) and risk factors of each navy mission. Also, we have developed two preventive maintenance policies based on a dynamic failure law for a finite planning horizon. The first preventive maintenance policy is sporadic the aim is to determine the number of maintenance activities to perform and the choice of the mission that must be followed by preventive activity, in order to minimize maintenance costs. The optimal solutions are obtained by the extended great deluge algorithm . The second preventive maintenance policy is periodic. With a numerical procedure, we established the optimal number of maintenance actions . In a recent research work, based on a finite time horizon, we have extended this work by considering a production system, which must satisfy a set of requests. The objective is to determine the best production plan to minimize both the holding costs and maintenance costs .
Another research program focuses on the development of models considering maintenance imperfections. Indeed, most preventive maintenance models assume that the system is restored to as good as new at each maintenance actions and consider the intervention time as negligible. Hence, the system may not be restored to as good as new immediately after the completion of maintenance action. Our approach is based on a fuzzy logic model which allows taking into account imperfections. These later are essentially due to technician's experience, the level of complexity of the restoration, and the time taken by maintenance actions. After a maintenance, the machine returns to an age between as good as new and as bad as old. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation-based optimization is used to have a more reactive and accurate tool for parishioners. By taken into account the impact of the imperfections due to human factors, the period for the preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by a simulation-based optimization .
Uncertainty in a machine reliability is commonly present, but not always completely modeled. In general, statistical laws like Weibull, normal or exponential probability distribution are used, but they only model randomness of the reliability and not the fuzziness. Additionally, the systems parameters like the costs and durations of maintenance, are used to be taken like real constants without taking into account the unsharpness and imprecision of these parameters. In our work, the concept of fuzzy random probability distribution is also introduced to the maintenance model to cover in a wider level the uncertainty of the reliability of the machines. This method gives a better representation of the randomness as well as the fuzziness of the reliability. The systems parameters are modeled not as real constants but as fuzzy constant numbers to introduce their unsharpness. Finally the goal is to minimize the total cost of maintenance per unit time, by finding the optimal age at which preventive maintenance must be performed in a fuzzy environment .
For maintenance decision support, we are working on data and information collection, in terms of production systems' health information chain modeling and data analysis, and in terms diagnosis and prognosis tasks scheduling and optimization . These investigations aim at equipments', maintainability and reliability data quality enhancement, health monitoring, risks evaluation, maintenance actions decidability studies and lifecycle management , . This is a new topic of investigation that was recently initiated to address e-maintenance problems arising from the implementation of advanced Information and Communication Technologies for the maintenance decision support.
The Network of Excellence for Innovative Production Machines and Systems (I*PROMS: see
http://
At present, I*PROMS comprises 30 member institutions representing 14 European countries and the coordinator is Cardiff University (United Kingdoms)
To realize the 'Autonomous Factory' vision of I*PROMS, the Network will vigorously prosecute research in four integrated areas spanning the whole field of production equipment and technologies. These integrated areas, referred to as clusters, are 'core competency areas' and I*PROMS will invest the necessary finances and resources to support them. They comprise:
Advanced Production Machines (APM) Cluster: will research into the innovative and readily reconfigurable machines and systems and efficient manufacturing processes needed to deliver high-quality products competitively in the future.
Production Automation and Control (PAC) Cluster: will examine control issues associated with the 'Autonomous Factory' and the new ICT-based paradigms and algorithms needed to realize autonomy robustly and cost-effectively.
Innovative Design Technology (IDT) Cluster: will focus on activities that are traditionally upstream with respect to manufacturing and develop novel collaborative tools and techniques to bring design closer to manufacturing, thus producing gains in competitiveness through maximizing concurrency.
Production Organization and Management (POM) Cluster: will develop the innovative methodologies necessary to achieve manufacturing competitiveness. It will address the effective integration of human and technical resources. POM will create new sustainable management strategies for cost-effective and rapid reconfiguration of the Factory.
Members of COSTEAM contribute to clusters PAC and POM.
ADENTS
COSTEAM and the enterprise ADENTS collaborates in the scope of the traceability management. Simòn Tamayo, PhD student, works with a "contrat CIFRE" in this context.
LAGIS (Laboratoire d'Automatique, Génie Informatique et Signal) - École Centrale de Lille
Our team collaborate with Professor Étienne Craye, Manager of the SED (Systèmes à Événements Discrets) team of LAGIS, on the subject of controller synthesis for an application in the domain of reconfiguration.
LAI (Laboratoire d'Automatique Industrielle) - INSA Lyon (National Institute for Applied Sciences)
Professor Nidhal Rezg works in collaboration with Professor Eric Niel (manager of the team "Dependability and Monitoring of Industrial systems" of LAI) on the subject of control tolerating failures.
ORCHIDS (Operations Research for Complex Hybrid Decision Systems) - LORIA - INPL
COSTEAM and Orchids collaborates in the scope of the supply chain management.
SLP (Systèmes Logistiques et de Production) - IRCCyN
(Institut de Recherche en Communication et Cybernétique de Nantes)
Nathalie Sauer works in collaboration with the SLP team of IRCCyN, especially for which concerns management and development of production systems. She has also been ending the supervision of a PhD student of this lab whose thesis is related to evaluation and optimization of event graphs.
CRAN and ICD
Kondo H. Adjallah works in collaboration with the CRAN team of Prof. Ragot, the ICD research team of Dr Snoussi and Birragah on data collection management on large structure based on wireless sensors network for maintenance decision support. This collaboration was in the framework of the COSMOS project (design and monitoring of reliable and multiple operation modes systems).
ESSTT (École Supérieure des Sciences et Techniques) - Tunis-Tunisia
Professor Nidhal Rezg collaborates with Prof. Anis Chelbi from ESSTT-Tunisia. This collaboration consists at undertaking research studies in order to develop new industrial integrated maintenance/production strategies with the aim of improving the traditional strategies.
ETSMTL (École de Technologie Supérieure) - Montreal-Canada
Professor Nidhal Rezg and Dr. Dellagi collaborate with Professor Ali Gharbi in the maintenance field.
FUCaM (Catholic university of Mons) - Belgium
Professor Nidhal Rezg collaborates with Dr. Fouad Riane (manager of the Center of Research and Studies in Industrial Management) in the field of healthcare management.
IIT (Indian Institute of Technology) - Delhi-India
India Since January 2007, Dr. Lyes Benyoucef is collaborating with Professor S. G. Deshmukh and Dr. Vipul Jain from the Department of Mechanical Engineering (IIT-Delhi) on the development of new approaches to address the issue of agility and lean in dynamic integrated supply chains.
IIT (Indian Institute of Technology) - Kharagpur-India
Since January 2008, Dr. Lyes Benyoucef is collaborating with Professor M.K. Tiwari from the Department of Industrial Engineering and Management (IIT-Kharagpur) on the development of new approaches to address the management of reconfigurable manufacturing systems.
ISB (Indian School of Business) - Hyderabad-India
Since May 2006, Dr. Lyes Benyoucef is collaborating with Dr. Kameshwaran Sampath from the Center for Global Logistics and Manufacturing Strategies (ISB-India) on the development of new techniques to solve some complex optimization problems present in E-Procurement environment.
RM (Reliability and Maintenance Network) - Canada
Partners of the RM network, the University of Laval (Canada), the Polytechnic School of Montreal (Canada), the Higher School of Science and Technology of Tunis (Tunisia) and COSTEAM, exchange their industrial and scientific experiences and results on reliability and maintenance of production systems.
Technological Institute of Celaya - Mexico
The staff of our team-project, and especially Professor Nathalie Sauer, collaborates with Professor Sergio Martinez (manager for research and development) on the subject of performance evaluation for the management of complex systems. The officialising of this collaboration throughout a contract granted by the Mexican government is under consideration.
University of Quisqueya (Haïti)
COSTEAM and University of Quisqueya collaborates in the scope of the service system control and engineering. Norly Germain, PhD student, works with a "co-tutelle" in this context.
University of Moncton - Canada
Professor Nidhal Rezg, collaborate with Professor Gilles Cormier. This collaboration consists on developing new industrial integrated maintenance/production strategies.
USA NSF (I/U-CRC IMS), University of Cincinnati-USA
Since 2000, Kondo H. Adjallah has close collaboration with the USA NSF Industry/University Cooperative Research Center for Intelligent Maintenance Systems Center (I/U-CRC IMS), University of Cincinnati, Ohio. He invited Prof. Jay Lee, head of the IMS Center, on June 17-18, 2009, to deliver two days seminars on "Intelligent maintenance systems development" and on "dominant innovation strategy", to the laboratory members.
Federal Institute of Technology of Lausanne, Switzerland
Since 2000, Kondo H. Adjallah collaborates also with Dr. Dimitris Kiritsis, head of the LICP research lab, at the Swiss Federal Institute of Technology of Lausanne (EPFL). Dr. Kiritsis was invited on June 23, to deliver a seminar on "Emerging Technologies for Asset Lifecycle Management" to the laboratory members. This collaboration accounts for investigations on data collection process modeling and decision support to predictive maintenance.
Kondo H. Adjallah has been invited to deliver keynote speech at the International Conference on Computers and Industrial Engineering, Troyes, France, July 6-8, 2009, on "Requirements and issues of Data and Information for the Decision Support in Industrial Asset Management". He has also been invited by Prof Jay Lee to deliver a keynote speech at the NSF I/U CRC IMS 17th industrial advisory board meeting, Michigan, May 6, 2009, on the Management of the durable Infrastructures of Development in the framework of a cooperative research. Moreover, he has been invited to deliver keynote speech at the International Workshop DMD'2009 (Diagnostic et Maintenance des Systèmes Décentralisés), Maitrise des Infrastructures des Eaux et des Energies Renouvelables, Yaoundé, Cameroun, 27-28 avril 2009, on "Eco-maintenance : Approche scientifique, économique et écologique de gestion intégrée du cycle de vie des infrastructures"
Kondo H. Adjallah co-lead with Zined Simeu Abazi of the G-SCOP lab, the Working Group MACOD of the GDR MAC, at national level. He is also leading the activities of the international cooperative research network for the Management of the Infrastructures of Development (ICRN MID), which involves research laboratories 7 High Education institutions in the world.
Most of the members of our project regularly participate in working groups of GDR-MACS (such as Bermudes, FL, RdP, CSP, INCOS, ORT, META, STP, GISEM) and Nidhal Rezg is the leader of INCOS group (Control and supervision engineering of discrete event systems). The GDR-MACS has vocation to federate the community of the researchers in industrial engineering, by extremely interdisciplinary nature.
Many members of the team are members of the ROADEF (French Operational Research and Decision Support Society). Nathalie Sauer is member of the new board of this Society.
Nidhal Rezg was member of the program committee of PENTOM'09 (Performance et Nouvelles Technologies en Maintenance) and IESM'09 (4th International Conference on Industrial Engineering and Systems Management).
Lyes Benyoucef was member of the international program committee for IESM'09 (4th International Conference on Engineering and Systems Management). He is also editorial board member of IJSOI (International Journal of Services Operations and Informatics), IJBPSCM (International Journal of Business Performance and Supply Chain Modeling) and JOL (Journal of Operations and Logistics).
Sophie Hennequin has participated to the international program committee of ISC'09 (Industrial Simulation Conference), which has been held in Lyon, France and of IMETI'09 (2nd International Multi-Conference on Engineering and Technological Innovation) which has been held in Orlando, USA. She has also participated to the international reviewer committee of the CIE39 conference (International Conference on Computers and Industrial Engineering), which has been held in Troyes (UTT), France, July 6-8, 2009.
Kondo H. Adjallah has organized special sessions for international conferences INCOM'2009 IFAC (Moscou), CIGI'2009 (France), CIE'39 (France). He also served in the jury board of the Master Thesis Award in Maintenance for the European Federation of National Maintenance Society (EFNMS).
Members of the team are reviewers this year for the following journals: International Journal of Advanced Manufacturing Technology, IEEE Transactions on Automatic Control, Computer and Education, International Journal of Production Research, Decision Support System, Discrete Event Dynamic Systems, International Journal of Production Economics, International Journal of Advanced Manufacturing Technology, Journal Européen des Systémes Automatisés, Discrete Optimization, and for the following conferences: IMETI'09 (International Multi Conference on Engineering and Technological Innovation), CIE39 (International Conference on Computers and Industrial Engineering).
Members of the team organize with the LGIPM (Laboratoire de Génie Industriel et Production de Metz) the 8th International Conference on Modelling and Simulation, Hammamet, Tunisia, May 10th-12th, 2010.