These systems (see Figure 1) are industrial systems (production systems, value chains, ecoparks), complex products (airplanes, cars…), complex factories, transportation systems, health systems, energy networks, service systems and construction systems.

Figure 1: LGI studies production, activity or social-technical systems along their life cycles

Figure 2: Life Cycle Assessment & Eco-Design of complex industrial systems

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Key principles of our research are: multidisciplinarity, life-cycle thinking (see Figure 2), societal and economical issues, model-based engineering approaches.

•  the presence of sophisticated technical components but also of human agents (organizations, policy makers, operators),
• a large number of individual components that interact,
• heterogeneity of these components, each with specific individual behavior,
• systems that must often be analyzed at different physical, spatial and temporal scales and from different points of view (technical performance, cost, environmental impacts, material flows, skills...), see for instance Figure 3,
• a system feedback on its components and the emergence of macroscopic properties.

Figure 3: Simulation of a kitting automated cell (robot-operator collaboration upstream of an assembly line)

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The control of such systems presents many challenges and issues from both a technical and scientific point of view as well as practical and application perspectives like financial profitability, efficiency, continuity and reliability of service, security, resilience. The integration of technical systems is already challenging regarding, for example, aerospace, automotive or energy systems, but it is even more complex when it comes to inter-network systems («System of Systems» paradigm) such as health systems, human mobility infrastructure, distribution of products and services, transport and regulation of energy, gas, water, and other socio-technical systems including human or various agents such as organizations with different and even contradictory strategies, goals and preferences. Our scientific approach consists in adequately modeling for analyzing and simulating (see Figure 4) in order to better understand the system behavior through virtual experiments on models and, ultimately, finding optimal solutions for the design, deployment and monitoring. Often many life cycle phases of these systems must be modeled and analyzed: collection of needs and requirements specification, development (architectural design, dimensioning, validation, manufacture and market launch or startup), system management (its regulation, its maintenance, its failure modes, its upgrade, its dismantling and end of life).

Figure 4: Optimization of patient flows in emergency services

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Collaboration