Production scheduling in a context of regulation of energy consumption

We will study the integration of clean/renewable energies when carrying out production plans in an economically efficient way and propose decision-making methods that will help production managers.
For this we will start from the scheduling problems with job acceptance/rejection and explore the mathematical structures and develop optimization algorithms allowing us to extend the results to more general situations such as encountered in larger production workshops.
Production scheduling in the industrial sector is the process of organizing the completion of a series of jobs over time, taking into account the various production constraints. The scheduling and planning of tasks and processes brings together several aspects that go through the study, development and optimization of production lines as well as the establishment of an organization of flows that must be optimal, both for raw materials and for finished products. It is also a question of taking into account customer requests, delivery times, without forgetting the adaptation to possible anomalies or incidents throughout the production cycle. Taking energy consumption and greenhouse gas emissions into account when planning and scheduling production is clearly the orientation to be given within the framework of the energy sobriety plan. Recent work in the field of Operations Research has shown the relevance of integrating them in order to offer decision-making support tools adapted to the various situations that industries have to face.
Throughout this doctoral thesis, we will study the impacts of the availability of energies (in particular clean/ renewable energies), the ways of adapting the production plan in an economically efficient way and proposing methods to help the decision that will come to help production managers.

Research Unit LIST3N (Laboratoire Informatique et Société Numérique)

Team : Optimisation

Web site LIST3N : https://recherche.utt.fr/list3n

Expected skills:
– Ease in algorithms and with exact and approximate optimization methods of operational research,
– Development of applications in C, C++ or JAVA,
– Master’s degree in computer science, applied mathematics, industrial engineering, data sciences or a close field, – teamwork and autonomy