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2023, Proceedings of the 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Pages -1588

Task Scheduling in Assembly Lines with Single-Agent Deep Reinforcement Learning (04b Atto di convegno in volume)

Imran Muhammad, Antonucci Giovanni, Di Giorgio Alessandro, Priscoli Francesco Delli, Tortorelli Andrea, Liberati Francesco

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.
ISBN: 979-8-3503-1140-2
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