Le 20/04/2021 par SimonT :
Ph.D. Position in Operations Research
Institution : IMT Atlantique, Nantes. Laboratoires des Sciences du Numérique de Nantes (LS2N), team SLP.
Topic: Machine learning for decision aid support in manufacturing.
IMT Atlantique is a top level engineering school, a technical university, under the aegis of the Ministry of Industry and the digital sector formed from the merger of two renowned schools (Te?le?com Bretagne and E?cole des Mines de Nantes). It focuses on digital technology, energy and the environment with the objectives of contributing to economic development through education, outstanding research and innovation. Since its creation on January 1, 2017, IMT Atlantique has inherited all of the research and innovation activities of Télécom Bretagne and École des Mines de Nantes. This new establishment comprises 13 departments of teaching and research, involved in six research labs. With more than 1000 publications each year (400 of which are A Rank), the research at IMT Atlantique is carried out by 290 permanent researchers and lecturers, 110 non-permanent researchers and over 300 doctoral students.
The SLP (Logistics and Production Systems) team is part of the Optimization and Decision Support group of the Department of Automation, Production, and Computer Science. The team focuses on the design and optimization of production systems, logistic and transport networks, planning and scheduling of production activities, and risk management for industrial systems and services.
IMT Atlantique is seeking a PhD student to join the H2020 European funded ASSISTANT project (3-years). The ASSISTANT (LeArning and robuSt deciSIon SupporT sytems for agile mANufacTuring environments) consortium is composed of eleven academic and industrial partners combining key skills in artificial intelligence, optimization, manufacturing, industrial engineering, edge computing and robotics. ASSISTANT aims to create intelligent digital twins through the joint use of machine learning (ML), optimization, simulation and domain models. The resulting tools will design and operate complex, collaborative, and reconfigurable production systems based on data collected from various sources such as IoT devices. ASSISTANT will experiment this methodology on a significant panel of use cases selected for their relevance in the current context of the digital transformation of production in major manufacturing sectors undergoing a rapid transformation like energy, industrial equipment, and automotive sectors which already make extensive use of digital twins. ASSISTANT targets a significant increase in flexibility and reactivity, product/process quality, and robustness of manufacturing systems by integrating human and machine intelligence in a sustainable learning relationship.
Thesis description: Within the ASSISTANT project, this thesis aims to enhance models classically used in operation research for manufacturing applications thanks to machine learning. Many manufacturing decisions (line balancing, planning, scheduling) rely on mathematical models (e.g., a linear program) to suggest decisions to shop floor managers, production directors, … Often, these models provide an approximation of the complexity of the shop floor. While this approximation allows solving the model efficiently, it often leads to errors. In addition, production systems are nowadays more reconfigurable, and thus in constant evolution. Modifying the optimization model to cope with these constant changes is a burden. To alleviate these issues, the goal of the thesis is to develop tools to automatically acquire parts (constraints, objectives) of the optimization models by translating the functions (linear regression, regression trees, …) learned from data into linear inequalities. As a result, the tools can take advantage of the massive amount of data generated on the shop floor and external data sources to yield better decisions. The Ph.D. student will validate the performance of the resulting approach by deploying the tool in a use case of ASSISTANT (SIEMENS Energy, PSA, Atlas Copco).
Start: September - December 2021
Supervisor: Alexandre Dolgui, Simon Thevenin.
Contact: CV, letter of motivation, master grads, and recommendation letters must be sent to email@example.com