[STAGE MASTER 2] - Two-level dynamic scheduling for a reconfigurable production system

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Introduction to the laboratory CESI LINEACT- Research Unit

CESI LINEACT (Digital Innovation Laboratory for Companies and Learnings at the service of the territories competitiveness) is the CESI group laboratory whose activities are implemented on CESI campuses.

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CESI LINEACT (EA 7527), Digital Innovation Laboratory for Business and Learning at the service of the Competitiveness of Territories, anticipates and accompanies the technological mutations of the sectors and services related to industry and construction. CESI's historical proximity to companies is a determining factor for our research activities and has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as the territorial network and the links with training, have allowed us to build transversal research; it puts the human being, his needs and his uses, at the center of its problems and approaches the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific themes and two application areas.

• Theme 1 "Learning and Innovation" is mainly concerned with Cognitive Sciences, Social Sciences and Management Sciences, Training Sciences and Techniques and Innovation Sciences. The main scientific objectives of this theme are to understand the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems, etc.) on the learning, creativity and innovation processes.
• Theme 2 "Engineering and Digital Tools" is mainly concerned with Digital Sciences and Engineering. The main scientific objectives of this theme concern the modeling, simulation, optimization and data analysis of industrial or urban systems. The research work also focuses on the associated decision support tools and on the study of digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

• Industry 5.0,
• Construction 4.0 and Sustainable City,
• Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Links to the research axes of the research team involved

CESI Lineact Research Thematic: Decision Support for Production Systems

Description

Keywords: RMS, Scheduling, Bi-level optimization, Discrete-event Simulation

Description

In an industrial context characterized by growing demand uncertainty, high product variability, and significant cost and deadline constraints, Reconfigurable Manufacturing Systems (RMS) offer a promising alternative to traditional production systems. In addition to their flexibility and resilience in the face of uncertainty, RMS also offer significant potential for addressing sustainable development challenges by promoting better use of resources, reducing energy consumption, and limiting the environmental footprint throughout the production cycle.

RMS, characterized by their modularity and reconfigurability, require dynamic resource management to maximize performance while ensuring the flexibility needed for frequent changes. Two-level dynamic scheduling meets this requirement by combining :

• Strategic planning, which defines the optimal resource configurations over the medium or long term [1, 2], and
• Operational management , which organizes daily tasks and operations in real time [3, 6, 7].

This dual approach not only ensures efficient resource allocation, but also guarantees an appropriate response to disruptions, whether internal (breakdowns, human error) or external (changes in demand). Thus, dynamic two-level scheduling represents a promising approach to managing the complexity and uncertainty inherent in RMS, combining performance stability (economic, environmental, and societal) with flexibility in the face of frequent changes.

The issue concerns the coupling between the strategic and operational levels. This coupling is particularly complex in RMS due to their ability to change variety and production capacity [4]. The two-level approach must therefore take this specificity into account.

In order to anticipate configuration changes at the strategic level, it is essential to assess the room for maneuver of a given configuration. In this context, the notion of a “flexibility corridor” emerges as a key concept for characterizing this margin for action [5]. These “flexibility corridors” are still poorly formalized in the literature and therefore deserve to be precisely defined and quantified, taking into account the constraints of reconfiguration, capacity, and profitability. This notion of a “flexibility corridor” is a potential decision-making tool for proactively anticipating and managing reconfigurations.

Objectives

1. Two-level modeling: Develop a mathematical model that integrates strategic objectives such as reducing costs (upper level) and operational scheduling (lower level), while taking into account the specific characteristics of RMSs, such as scalability (the ability of a system to adapt its production capacity) and convertibility (the ability of a system to absorb disturbances, such as machine breakdowns or demand fluctuations)

2. Simulate different production scenarios incorporating disruptions in order to evaluate the trade-off between system performance and flexibility.

3. Identify the profitability limits of a given configuration as a function of customer demand while maintaining an acceptable level of performance → Define “flexibility corridors” for the considered use case.

Expected scientific/technical production

1. A mathematical framework integrating strategic objectives and operational scheduling, accounting for RMS features such as scalability and convertibility.

2. A simulation method evaluating system performance and flexibility under disruptions

3. A decision tool for sizing flexibility corridors

4. A scientific report or publication draft, presenting the methodology, results, and implications for large-scale mobility optimization.

Bibliography

1. Ashraf, M., Hasan, F.: Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology 98(5), 2137–2156 (2018)

2. Haddou Benderbal, H., Dahane, M., Benyoucef, L.: Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (rms) design under unavailability constraints. International Journal of Production Research 55(20), 6033–6051 (2017)

3. Ning, T., Huang, M., Liang, X., Jin, H.: A novel dynamic scheduling strategy for solving flexible job-shop problems. Journal of Ambient Intelligence and Humanized Computing 7(5), 721–729 (2016)

4. Yelles-Chaouche, A.R., Gurevsky, E., Brahimi, N., Dolgui, A.: Reconfigurable manufacturing systems from an optimisation perspective: a focused review of literature. International Journal of Production Research 59(21), 6400–6418 (2021)

5. Azab, A., ElMaraghy, H., Nyhuis, P., Pachow-Frauenhofer, J., Schmidt, M.: Mechanics of change: A framework to reconfigure manufacturing systems. CIRP Journal of Manufacturing Science and Technology 6(2), 110–119 (2013)

6. Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria. Computers & Industrial Engineering, 110419 (2024)

7. Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Dynamic and sustainable flexible job shop scheduling problem under worker unavailability risk. In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), pp. 1126–1131 (2024). . 10711610

Profil

The Candidate's Profil

The candidate should be a Master’s student (M2) or in the final year of an Engineering School program, with a background in industrial engineering, operational research and optimization

She/He should have some knowledge and experience in a number of the following topics:

• Mathematical modeling
• Optimization/ meta heuristic algorithm
• Simulation
• Programming skills
• Good written and oral communication skills in English

Additional knowledge in discrete-event simulation and tools (Flexsim for example) would be an advantage.

Prise de fonction

Dès que possible