A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products
Résumé
Modern manufacturing systems need to be increasingly flexible, agile and adaptable. In this paper, we propose an original approach combining ac-tive/intelligent product architectures with learning mechanisms to ensure flexi-bility and agility of the entire manufacturing system. Using learning approaches such as Reinforcement Learning (RL), an active product can reuse experience learned to enhance its decisional capability. A contextualization method is pro-posed to improve product decision making for scheduling tasks. The approach was applied to a case study using a multi-agent simulation platform.