LORENTE, Quentin; VILLENEUVE, Eric; MERLO, Christophe; BOY, Guy Andre; THERMY, Francois

doi:10.1109/IEEM55944.2022.9989843

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LORENTE, Quentin
ESTIA INSTITUTE OF TECHNOLOGY

VILLENEUVE, Eric

ESTIA INSTITUTE OF TECHNOLOGY

MERLO, Christophe

ESTIA INSTITUTE OF TECHNOLOGY

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Communication dans un congrès

Ce document a été publié dans

IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2022-12-07, Kuala Lumpur. 2022-12-26

IEEE

Résumé en anglais

This research work aims at improving collective decision-making and learning through a digital twin of the organization in the context of a complex industrial activity such as helicopter engine maintenance. Field and bibliographic studies allowed to determine that the digital twin should be based on a multi-agent system model for reasons of flexibility and modularity necessary in this constantly changing environment. The digital twin is intended to adapt to the organization but also to enhance it by including missing information flows. This paper presents the agent model chosen and inspired from reinforcement learning and how it allowed to identify these missing flows. The importance of interfaces in the digital twin and what they should contain to integrate agents is shown, as well as the psychosocial aspects to be considered for humans to handle their design.< Réduire

Mots clés en anglais

Adaptation models

Decision making

Helicopters

Collaboration

Psychology

Maintenance engineering

Digital twins