Deep Reinforcement Learning-Based Approach to Dynamically Balance Multi-manned Assembly Lines

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Santos, R (Autor) Outra; Marques, C (Autor) Outra; Toscano, C (Autor) Outra; Ferreira, M (Autor) Outra; Ribeiro, J (Autor) Outra

Ata de Conferência Internacional

Título: Lecture Notes in Mechanical Engineering

Páginas: 633-640

32nd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2023

Porto, 18 June 2023 through 22 June 2023

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Y-X6X

DOI: 10.1007/978-3-031-38241-3_71

Abstract (EN): Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7