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Home   /   Thesis   /   Scheduling adapted to R&D cleanrooms

Scheduling adapted to R&D cleanrooms

Computer science and software Engineering sciences Factory of the future incl. robotics and non destructive testing Technological challenges


In the midst of digital transition, characterized by the rapid development of new technologies, this subject aims to participate in the automation of decision support processes in a dynamic and complex environment, and to reduce cycle times in an R&D environment, increasingly subject to an injunction of efficiency.
In the current context marked by great uncertainty and multiple crises (geopolitical, economic and ecological), the microelectronics manufacturing industry is facing global competition. To face these challenges, this thesis project aims to develop resolution methods for a scheduling problem on complex machines within flexible workshops, taking into account the dynamic nature and complexity of the microelectronics production environment.
The objective is to propose approaches offering robust and industrially relevant solutions to meet these challenges. Robustness will be assessed using appropriate risk indicators, while industrial relevance will be measured via previously identified performance indicators. The proposed resolution approaches will be evaluated through numerical experiments carried out on benchmark instances and industrial cases, and more particularly on the WIP of LETI cleanrooms. In particular, taking into account equipment campaigns and long downs will be one of the work areas of the thesis. The specificities of the different workshops (high work in progress and short processing time, limited work in progress with long processing time) will also be studied in order to provide a global tool, adapted to R&D clean rooms, and efficient.


Département des Plateformes Technologiques (LETI)
Service Méthodes, Interface Technologique et Logistique
Laboratoire Logistique, Approvisionnement et Méthodes
Université de Lyon
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