PhD in Computer Science and Industrial Engineering (M/F)

Job Description

Thesis Subject

Dynamic Optimization Methods for Remanufacturing: Integrating Human Factors and Uncertainty into the Operational Management of Reverse Logistics Flows

Your Work Environment

Host institution: Mines Saint-Étienne (MSE) - Henri Fayol Institute
Laboratory: LIMOS (UMR CNRS 6158)
Funding: ANR Project ReCircle: Remanufacturing and Circular Economy Logistics
Project partners: MSE, IMT Nord Europe (IMT-NE), Université Clermont Auvergne (UCA)
Supervision Damien Lamy, Xavier Delorme (MSE) - Mohammed-Amine Abdous (IMT-NE)

The objective of the PhD project is to propose approaches for continuously re-optimizing the operational decisions of a remanufacturing workshop in response to disruptions, while maintaining performance in terms of cost, service quality, ergonomics, and environmental impact. Two research focus are proposed:

Focus 1: Dynamic optimization method for combinatorial problems
This axis will examine the different approaches in the literature applied to dynamic problems (reactive, real-time, online, etc.) and propose an approach applicable to different problem typologies. The work will involve:
• Exploring real-time re-optimization mechanisms in response to disruptions;
• Leveraging partial information to forecast possible future disruptions and anticipate suitable solutions;
• Identifying generalizable elements and their limitations, especially when relevant information is unavailable;
• Proposing improvements in solution quality based on hybrid methods: optimization/learning and optimization/simulation.

Focus 2: Application to real-time scheduling of a dual-resource flexible job-shop remanufacturing workshop under uncertainty
This axis will address the scheduling of a remanufacturing workshop characterized by dual resources (a machine and a qualified operator) and uncertain process routes (conditional operations, rework, variable durations). As humans are central to such a system, the performance objectives must include a strong ergonomic component (fatigue, break times, etc.). The work will involve:
• Modelling the problem in a static context and solving it using heuristic and metaheuristic approaches;
• Adapting and validating the real-time optimization approach while accounting for constraints related to human resources;

Constraints and risks

• Research Master's degree or engineering degree (five years of higher education) in Operations Research, Computer Science, or Industrial Engineering.
• Strong knowledge of combinatorial optimization: scheduling (job shop, flow shop), metaheuristics (local search methods, evolutionary algorithms, etc.), and mathematical modelling (MILP).
• Knowledge of production management, reverse logistics, or human factors would be appreciated. An interest in discrete-event simulation would also be an asset.
• Programming skills (C++, Java, or Python).
• Autonomy, scientific rigor, curiosity, and the ability to work in a multi-site collaborative context.
• Proficiency in scientific English (reading and writing articles).

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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