Internship OPTIMAN : Integration of Production Scheduling into an Industrial Digital Twin for a Flexible and Robotised Machining Cell CESI

Laboratory presentation

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI to companies is a determining factor in our research activities. It 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 territorial networking and links with training, has enabled the construction of cross-cutting research; it puts humans, their needs, and their uses at the center of its issues and addresses the technological angle through these contributions.

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

• Team 1 'Learning and Innovating' mainly concerns Cognitive Sciences, Social Sciences, and Management Sciences, Training Techniques, and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and, more particularly, of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity, and innovation processes.

• Team 2 'Engineering and Digital Tools' mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization, and data analysis of cyber-physical systems. Research also focuses on decision-support tools and on the study of human-system interactions, particularly through 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 the one in Rouen dedicated to Factory 5.0 and the one in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Abstract

This internship focuses on the integration of a production scheduling system into an industrial digital twin of a robotic machining cell, within a human-centred Industry 5.0 framework. The objective is to ensure a tight and efficient coupling between the digital twin, a real robotic cell, and a physical instrumented mock-up developed at CESI, enabling real-time or near-real-time supervision, control, and decision support.

The production schedule is generated by an optimisation-based scheduling module implemented in Odoo. A key challenge of the internship is to design an efficient data exchange architecture allowing fast access to scheduling data and seamless communication between Odoo, the digital twin, and the physical systems. The intern will be responsible for integrating the scheduling outputs into the digital twin, synchronising virtual and physical states, and ensuring consistency between planned operations and real execution on both the CESI mock-up and the industrial cell operated by Maugars Industrie.

Beyond technical integration, the internship adopts a human-centred perspective: the digital twin must support decision-making by providing clear visualisation of schedules, system states, and performance indicators, while allowing operators and engineers to interact with the system (e.g. scenario analysis, schedule updates, supervision of critical operations).

This internship is conducted within the OPTIMAN project led by CESI LINEACT, and aims to contribute to the development of a connected, interoperable, and operational digital twin tightly coupled with an industrial scheduling system.

Research Work

Industry 5.0 represents a strategic shift in production systems by combining advanced technologies-such as digital twins, collaborative robots and IoT-with a strong human-centred focus. Its ambition is to enhance the resilience of value chains, support environmental sustainability and improve operator well-being through intelligent and ergonomic work environments. Recent works highlight the need for open interoperability models and seamless integration between physical and virtual layers, relying on real-time communication, standardized data architectures and adaptive interfaces tailored to operators' needs (Gumzej & Rosi, 2023; Alexa et al., 2022; Partarakis & Zabulis, 2024). Digital twins play a key role in this vision by enabling continuous interaction between physical assets and their digital counterparts, supporting supervision, optimisation, and adaptive decision-making. Recent research highlights the importance of interoperability, efficient data architectures, and real-time communication to ensure reliable integration between enterprise systems (e.g. ERP, schedulers) and cyber-physical production systems (Havard et al., 2021; Ji et al., 2025).

Research on collaborative robotics and digital twins shows that accurately modelling production cells makes it possible to simulate, monitor and remotely adjust complex industrial processes (Hémono et al 2024). When combined with human-aware planning approaches (e.g. fatigue-aware scheduling and break planning), these technologies contribute to reducing physical strain, improving safety and supporting more efficient decision-making on the shop floor (Chabane et al., 2023; Bouaziz, 2024; Hémono et al., 2025). In particular, the emergence of digital-twin-based control of robots offers a powerful way to manage smart systems interacting with human operators in increasingly uncertain industrial environments (Hémono et al., 2025).

Within this context, the OPTIMAN project aims to develop an operational digital twin of a robotic machining cell, connected both to a real industrial cell at Maugars Industrie and to an instrumented physical prototype at CESI. The integration of scheduling tools into this digital ecosystem is a major scientific and technical challenge, particularly in terms of data consistency, latency, and human-centred supervision.

The objective of this internship is to ensure the seamless integration of production scheduling within an industrial digital twin in order to enable real-time or near-real-time supervision and coordination between planning decisions and shop-floor execution. By coupling an optimisation-based scheduling module implemented in Odoo with a digital twin of a robotic machining cell, the proposed approach aims to improve the coherence between planned operations, system states, and actual production dynamics. This integration strengthens the responsiveness and robustness of the production system while supporting human-centred decision-making through transparent visualisation of schedules, system performance, and execution constraints. Relying on cyber-physical systems and high-performance communication architectures, the resulting framework enhances the agility of the workshop by facilitating rapid adaptation to demand variations and operational disturbances. In this sense, the internship fully contributes to the Industry 5.0 vision by combining scheduling performance, interoperability, and human-centric supervision to foster resilient and sustainable production systems.

his internship focuses on the development and integration of the scheduling layer within an existing digital twin connected to both an instrumented laboratory mock-up and an industrial robotic cell. Since the physical systems are already operational, the core objective is to design and implement an efficient data exchange and synchronisation mechanism between the Odoo-based scheduler, the digital twin, and the real production assets. The expected outcomes include:

• Integrating production schedules into the digital twin for coherent supervision of planned and executed operations.

• Ensuring fast and reliable communication between the digital and physical layers to support near-real-time monitoring and…

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