In digital transformation, Industrial Data Space (IDS) is a key enabler for industry 4.0 to improve the industrial process, efficiency, and energy consumption by exploiting learning-based techniques. The present paper focuses on improving the decision-making process in complex industrial environments by developing a Deep Reinforcement Learning (DRL) based real-time assistant. Mainly, we address a use case from the space industry to improve the launcher throughput and efficiency and reduce cost by optimally managing the industrial resources. A mathematical formulation of the Industrial Production System (IPS) and a simulated environment are developed to train the DRL-based Proximal Policy Optimization (PPO) agent. The proposed method is scalable and in line with the dynamic nature of the industrial production systems to overcome the domain-dependent heuristics extensively used in the manufacturing industry. Furthermore, simulation results show that the proposed method can provide industrial operators and managers with a real-time decision support system to increase the Return on Assets.
Task Scheduling in Assembly Lines with Single-Agent Deep Reinforcement Learning / Imran, Muhammad; Antonucci, Giovanni; Di Giorgio, Alessandro; Priscoli, Francesco Delli; Tortorelli, Andrea; Liberati, Francesco. - (2023), pp. 1583-1588. (Intervento presentato al convegno International Conference on Control, Decision and Information Technologies tenutosi a Rome; Italy) [10.1109/CoDIT58514.2023.10284428].
Task Scheduling in Assembly Lines with Single-Agent Deep Reinforcement Learning
Imran, Muhammad
;Antonucci, Giovanni
;Di Giorgio, Alessandro;Priscoli, Francesco Delli;Tortorelli, Andrea;Liberati, Francesco
2023
Abstract
In digital transformation, Industrial Data Space (IDS) is a key enabler for industry 4.0 to improve the industrial process, efficiency, and energy consumption by exploiting learning-based techniques. The present paper focuses on improving the decision-making process in complex industrial environments by developing a Deep Reinforcement Learning (DRL) based real-time assistant. Mainly, we address a use case from the space industry to improve the launcher throughput and efficiency and reduce cost by optimally managing the industrial resources. A mathematical formulation of the Industrial Production System (IPS) and a simulated environment are developed to train the DRL-based Proximal Policy Optimization (PPO) agent. The proposed method is scalable and in line with the dynamic nature of the industrial production systems to overcome the domain-dependent heuristics extensively used in the manufacturing industry. Furthermore, simulation results show that the proposed method can provide industrial operators and managers with a real-time decision support system to increase the Return on Assets.File | Dimensione | Formato | |
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