Design and control of reconfigurable and sustainable manufacturing systems – RECONFIDURABLE
Design and control of reconfigurable and sustainable manufacturing systems
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Challenges and objectives
The objective of RECONFIDURABLE is to demonstrate that reconfigurable manufacturing systems (RMS) can be a basis for developing sustainable production systems. Initially designed to offer a new type of flexibility to manufacturing systems, they have intrinsic properties for implementing a new generation of systems that meet sustainable development criteria. The methodology is based on the principle of RMS modularity. It involves choosing equipment modules to use and assigning production operations to these modules, taking into account possible scenarios of demand changes, the types of products to be manufactured and their constraints, as well as uncertainties regarding volumes during the system's life cycle. The methodology developed should extend the life of the production system and reduce emissions, energy consumption, and costs. The three stages considered are design, reconfiguration, and real-time management. The contribution does not aim to fully address all criteria, but rather to propose an approach that integrates sustainability with the other criteria of the three stages. The techniques used are based on multi-scale modeling, process modeling, combinatorial and uncertainty optimization, robustness analysis of the solutions obtained, discrete event simulation and learning techniques.
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The set of decision-making methods and tools allows: 1) To measure and integrate costs and environmental consequences into models for choosing equipment modules and reconfiguring an RMS. An important point is the coupling of cost/quality indicators with sustainability analysis. 2) To choose an optimal set of equipment modules for an RMS so that the system can evolve in the future and thus increase the possible options throughout its life cycle. This involves optimizing the system's ability to remain efficient and robust despite external changes. 3) To reconfigure an RMS optimally during its operation towards the new stage of its operation and to reschedule its production in the new configuration, taking into account the consequent changes in the environment, energy costs, etc.: To measure and model the environmental dimension, to choose a new set of modules to use while increasing the environmental efficiency of the system, etc. 4) Implement responsive and robust management in order to deal with specific internal and external hazards of various types: variations in demand and operating times, machine breakdowns, minor changes in energy consumption, hazards linked to the control of a new configuration or quality problems requiring reconfiguration.
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Battaïa, O.; Dolgui, A. Hybridizations in line balancing problems: A comprehensive review on new trends and formulations. International Journal of Production Economics. 2022, 250, 108673.
Arbi, S.; Cerqueus, A.; Gurevsky, E.; Dolgui, A.; Siadat, A. Simulated annealing for a bi-objective-based scalable balancing of a reconfigurable assembly line under uncertainty and limited resources. The 50th Computers and Industrial Engineering Conference. Sharjah, Oct 30–Nov. 2023, 1-4. (extended version submitted to Computers & Industrial Engineering).
Ostovari, A.; Benyoucef, L.; Haddou Benderbal, H.; Delorme, X. Robust configuration design of sustainable reconfigurable manufacturing system under uncertainty. The 20th IEEE International Conference on Networking, Sensing and Control. Oct 25-27, Marseille, IEEE. 2023, 1-6.
Ostovari, A.; Benyoucef, L.; Haddou Benderbal, H.; Delorme, X. Multi- objective workforce and process planning for socio-economic sustainable RMS: Lp-metric vs Epsilon Constraint. The 4th International Conference on Industry 4.0 and Smart Manufacturing. Nov 22-24, Portugal. Procedia Computer Science. 2023, 456-464.
Reconfigurable manufacturing systems (RMSs) are not only new manufacturing paradigm offering a customized flexibility. They are also a basis to develop a new generation of sustainable production systems. A promising way toward sustainable production passes through the design and intensive development of reconfigurable and sustainable manufacturing systems. The goal of this project is to develop an efficient methodology of decision-making support for the design and reconfiguration of such systems. The problem consists in an optimal selection of modules or pieces of equipment and an assignment of tasks to modules optimizing a given criterion while considering the required throughput and constraints as well as uncertainties. The project considers 3 main steps: design, reconfiguration and real-time control. For each of these 3 steps, we will develop and integrate sustainability criteria alongside conventional ones. In addition, two additional steps will be considered. The first represents a preliminary phase of identification and modeling of criteria to be evaluated and integrated. The second will be a test and validation step via industrial case studies. In this project, we will mainly focus on the environmental and economic dimensions of sustainability. So, our criteria will be technological (drift, quality, reliability), economical (cost, time, resources used), environmental (energy, emissions ...) and organizational (human factors, skills, safety and health ...). Our goal is not include all possible real life criteria but rather to propose an integrated approach of sustainability for design, planning and real-time reconfiguration of RMS while considering uncertainties. The techniques used will be based on multi-scale modeling, process modeling, combinatorial optimization and robust optimization as well as on stability analysis of the obtained solutions, discrete events simulation, and machine learning techniques.
Project coordination
Alexandre Dolgui (Laboratoire des Sciences du Numérique de Nantes)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partnership
LIS Laboratoire d'Informatique et Systèmes
LS2N Laboratoire des Sciences du Numérique de Nantes
LIMOS Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
AI AUTOMATIQUE ET INDUSTRIE / Service R&D
ENSAM - LCFC Ecole Nationale Supérieure d'Arts et Mértiers - LABORATOIRE CONCEPTION FABRICATION COMMANDE
Kedge Business School / Operations Management and Information Systems Department
Help of the ANR 577,684 euros
Beginning and duration of the scientific project:
February 2022
- 48 Months
