Digital-Twin-Enabled Framework for Training and Deploying AI Agents for Production Scheduling

Abstract

AbstractDigital manufacturing tools aim to provide intelligent solutions that will help manufacturing industry adapt to the volatile work environment. Modern technologies such as artificial intelligence (AI) and digital twins (DT) are primarily exploited in a way to simulate and select efficient solutions from a broad range of alternative decisions. This work aims to couple DT and AI technologies in a framework where training, testing, and deployment of AI agents is made more efficient in production scheduling applications. A set of different AI agents were developed, utilizing key optimization technologies such as mathematical programming, deep learning, heuristic algorithms, and deep reinforcement learning are developed to address hard production schedule optimization problems. DT is the pilar technology, which is used to simulate accurately the production environment and allow the agents to reach higher efficiency. On top of that, Asset Administration Shell (AAS) technology, being the pilar components of Industry 4.0 (I4.0), was used for transferring data in a standardized format in order to provide interoperability within the multi-agent system (MAS) and compatibility with the rest of I4.0 ecosystem. The system validation was provided in the manufacturing system of the bicycle industry by improving the business performance.