A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation
Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill‐and‐finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.
Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation
Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill‐and‐finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.
Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation
Hubert, Stefan (author) / Meintschel, Jonas (author) / Bleidorn, Dominik (author) / Ortmanns, Yak (author) / Wallrath, Roderich (author)
Chemie Ingenieur Technik ; 95 ; 1003-1011
2023-07-01
9 pages
Article (Journal)
Electronic Resource
English
Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning
| DOAJ | 2020
Robust Construction Scheduling Using Discrete-Event Simulation
| British Library Conference Proceedings | 2011
| DOAJ | 2025