PhD Thesis Proposal Digital Twin for Short Term Planning in Surface Treatment Industries

Global Information

This PhD is part of a supervision by IMT Mines Albi CGI team (France)

• Location: Albi, CGI  https://cgi.imt-mines-albi.fr/

• Expected start date: September or October 2026 depending on applicant availability

• Funding: CORAC PME Infinity

• Keywords :  Digital Twin engineering, coupling Simulation-optimisation-AI, surface treatment industry.

Context

In the context of Industry 4.0 and beyond, industrial stakeholders are deploying digitization strategies where operational systems are connected to increasingly intelligent cyber systems. In this context, the organizational digital twin refers to the system that enables bidirectional interaction between a physical organization (people and systems) and its virtual replica, with the goal of globally managing flows.

Recent reviews and typologies (e.g., Kritzinger et al., 2018; Onaji et al., 2022; Soori et al., 2023; Kober et al., 2024; Traoré, 2024; Namaki Araghi et al., 2025) on digital twins in manufacturing show that the digital twin manages workshop data and interoperates with various systems (MES, ERP, traceability, quality). It uses physics-based or data-driven simulation models focused on flow management, and orchestrates services oriented toward data processing or simulation models for decision support. The digital twin has various objectives (assessing the impact of changes and performance, optimization) and interacts with users as needed to support decisions at different time horizons (tactical and operational) and potentially of different natures (planning, maintenance, quality). Implementing such a system represents a major organizational and technical challenge that must be adapted to the specificities and evolving needs of industry.

The INFINITY project aims to design and test a digital twin system to support industrial planning in the context of the surface treatment industry. It is in partnership with a leading industrial player in this field for the aeronautics industry, Mecaprotec, and the project is funded by CORAC. Since large-scale industrial digital twin projects are rare, one of the project’s challenges is to study the scalability of the proposed system and the impact of the digital twin on the organization. As part of the project also focuses on designing new treatment processes, another aspect involves evaluating the impact of these new processes on the production system’s digital twin.

Several PhD are being conducted simultaneously within this project, each with complementary focuses:
 (i) data collection and the construction of necessary  informations for the digital twin;

 (ii) the digital twin for short- and medium-term planning (this PhD)

 (iii) the digital twin for strategic planning.

Problem Statement

This PhD thesis focuses on the digital twin for short- and medium-term production management in the surface treatment industry. This industry has several unique characteristics (such as make-to-order production on customer-supplied parts, very short lead times, a highly diverse customer base, and significant product variety) which shift the challenges of planning toward capacity agility to regulate work-in-progress between workshops.

The central objective of the thesis is to propose a digital twin system centered on finite-capacity simulation models to support short- and medium-term planning. It will involve designing complementary services to assist in this planning, including:

• Optimization of key decisions,          

• Skills development and Management of multi-skilled operators,

• Order grouping to form batch sizes,

• Detecting changes on critical parameters for the models (e.g., process routes, macro-routes, product mix, skills availability, etc.).

The digital twin will be designed using a systems engineering approach that:

• Formalizes requirements,

• Anticipates the impacts of scalability and process evolution to propose an architecture adapted to these changes,

• Evaluates the added value of the digital twin.

Research Problem Addressed in this Thesis

Several scientific challenges have been identified in this research topic:

• Coupling discrete-event simulation and equivalent models (reinforcement learning) for finite-capacity simulation: While this topic has been widely studied in recent years (refs), the challenge lies in proposing an efficient, effective, and frugal coupling tailored to the decisions to be supported and the specificities of surface treatment processes (some highly automated, others highly manual).

• Coupling finite-capacity simulation and optimization in a distributed control context: The coupling of simulation and optimization has long been studied for production systems (refs). Similarly, bi-level planning (short/medium-term) is well-established (refs). However, the models and resolution methods need to be adapted to the underlying industrial context. Here, as the production system consists of workshops with a degree of autonomy, the challenge is to propose local optimization approaches for each workshop across at least two time horizons (a few days and a few weeks), taking into account their specificities (skill management, order grouping), while maintaining global control of priorities and inter-workshop collaboration via the digital twin.

• A systems engineering approach applied to the digital twin, accounting for the impacts of scalability and technological evolution on the digital twin’s architecture.

Action Plan

• The main steps of the PhD include:

• Analysis of requirements and architecture of the digital twin,

• Definition of simulation models,

• Definition of optimization and coordination services,

• Application to a few examples from the industrial case study,

• Evaluation of the digital twin.

References

IMT Mines Albi and  CGI Laboratory

IMT Mines Albi, a school under the authority of the French Ministry of Industry, is part of the Institut Mines-Télécom, France’s leading group of engineering and management schools. At the forefront of industrial and academic challenges on the international stage, it acts as a scientific and economic driver for its region by combining its four missions—training engineers with a focus on sustainable development, conducting scientific research, contributing to economic development, and promoting the culture of science, technology, and innovation—into a virtuous and innovation-driven cycle.

Its position in education and research establishes IMT Mines Albi as a reference school in three of the IMT’s four thematic areas: future sustainable industries, energy - circular economy and society and, health and well-being engineering.

Through its Centre Génie Industriel (CGI), IMT Mines Albi conducts research at the intersection of artificial intelligence and industrial engineering, in collaboration with national and international public and industrial partners.

The Centre Génie Industriel (CGI) (cgi.imt-mines-albi.fr) comprises approximately 70 people, including 25 PhD students. The center focuses on supporting the transition of ecosystems by enabling responsible and sustainable decision-making in unstable or disrupted environments. This is achieved through the representation, modeling, and analysis of organizational data to formalize knowledge that leads to decision-making in heterogeneous, collaborative, uncertain, and/or disrupted contexts.

The CGI is structured around applied research axes and scientific programs. The applied research axes are:

• FLOWS: Flexible Logistics and Operations for Sustainable Worlds (this PhD is affiliated with this axis).

• DiSCS: Digital Systems for Crisis Management and Security;

• TRACE: Territorial Resilience, Agility, and Circular Economy;

• WHOPS: Well-being and Health through Organizational Processes and Services.

The two core scientific programs underpinning these research axes are:

• HOPOPOP: Hybridization for Operations & Planning, Organizations & Performance, Optimization & Problem-solving (this PhD is affiliated with the HOPOPOP program).

• AIME-DM: Automated Information Modeling and Extraction for Decision-Makers.

Profile and application:

Education

• Master’s or engineering degree in in Industrial Engineering (or computer science with experience in industrial engineering) with research experience.

Core competencies

• Systems Engineering

• Simulation and Operations Research

• Software development skills (Java, Python)

• Strong proficiency in English (minimum level B2) and French (minimum level B2)

Transversal skills

• Autonomy and ability to work collaboratively within a research team.

• Motivation to contribute to industrial application of research.

Additional desirable skills (not mandatory)

• Knowledge in reinforcement learning and data mining.

Application materials: CV, cover letter, summary of Master’s thesis or research work, transcripts, Recommendation letters (in particular in industry and research experience) and any other supporting documents.

Application deadline: June 7, 2026, 12:00 PM.

Notification for interview: no later than June 15th, 2026.

Contacts:

Jacques Lamothe, CGI IMT Mines Albi, jacques.lamothe@mines-albi.fr

Séverine Durieux, CGI IMT Mines Albi, severine.durieux@mines-albi.fr

Victor Romero, CGI IMT Mines Albi, victor.romero@mines-albi.fr