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Research Article

Explainable reinforcement learning in production control of job shop manufacturing system

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Pages 5812-5834 | Received 17 Feb 2021, Accepted 17 Aug 2021, Published online: 13 Sep 2021

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Marcel Panzer, Benedict Bender & Norbert Gronau. (2024) A deep reinforcement learning based hyper-heuristic for modular production control. International Journal of Production Research 62:8, pages 2747-2768.
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Fuqing Zhao, Xiaotong Hu, Ling Wang, Tianpeng Xu, Ningning Zhu & Jonrinaldi. (2023) A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. International Journal of Production Research 61:9, pages 2854-2872.
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Sharareh Taghipour, Hamed A. Namoura, Mani Sharifi & Mageed Ghaleb. Real-time production scheduling using a deep reinforcement learning-based multi-agent approach. INFOR: Information Systems and Operational Research 0:0, pages 1-25.
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Eungjin Kim, Taehyung Kim, Dongcheol Lee, Hyeongook Kim, Sehwan Kim, Jaewon Kim, Woosub Kim, Eunzi Kim, Younggil Jin & Tae-Eog Lee. (2024) Practical Reinforcement Learning for Adaptive Photolithography Scheduler in Mass Production. IEEE Transactions on Semiconductor Manufacturing 37:1, pages 16-26.
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Roham Sadeghi Tabar, Maria Chiara Magnanini, Florian Stamer, Marvin Carl May, Gisela Lanza, Kristina Wärmefjord & Rikard Söderberg. (2024) Selective disassembly planning considering process capability and component quality utilizing reinforcement learning. Procedia CIRP 121, pages 1-6.
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Susanne Lisa Piekarek, Alex Maximilian Frey, Marvin Carl May & Gisela Lanza. 2024. Production at the Leading Edge of Technology. Production at the Leading Edge of Technology 757 764 .
Marcel Panzer & Norbert Gronau. (2023) Designing an adaptive and deep learning based control framework for modular production systems. Journal of Intelligent Manufacturing.
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Cuixia Zhang, Fan Liu, Conghu Liu & Guangdong Tian. (2023) Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective. Environmental Science and Pollution Research 30:46, pages 102519-102530.
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Richard Dazeley, Peter Vamplew & Francisco Cruz. (2023) Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. Neural Computing and Applications 35:23, pages 16893-16916.
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Alexander Michael Kuhn, Marvin Carl May, Yuhui Liu, Andreas Kuhnle, William Tekouo & Gisela Lanza. (2022) Towards narrowing the reality gap in electromechanical systems: error modeling in virtual commissioning. Production Engineering 17:3-4, pages 535-545.
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Tingting Chen, Vignesh Sampath, Marvin Carl May, Shuo Shan, Oliver Jonas Jorg, Juan José Aguilar Martín, Florian Stamer, Gualtiero Fantoni, Guido Tosello & Matteo Calaon. (2023) Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’. Applied Sciences 13:3, pages 1903.
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Marvin Carl May, Sara Hermeler, Eric Mauch, Julia Dvorak, Louis Schäfer & Gisela Lanza. (2023) Reinforcement Learning for Improvement Measure Selection in Learning Factories. SSRN Electronic Journal.
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Leonard Overbeck, Valentin Glaser, Marvin Carl May & Gisela Lanza. 2023. Production Processes and Product Evolution in the Age of Disruption. Production Processes and Product Evolution in the Age of Disruption 338 346 .
Andrew Starkey & Chinedu Pascal Ezenkwu. 2023. Artificial Intelligence Applications and Innovations. Artificial Intelligence Applications and Innovations 94 105 .
Marvin Carl May, Lars Kiefer, Andreas Kuhnle & Gisela Lanza. (2022) Ontology-Based Production Simulation with OntologySim. Applied Sciences 12:3, pages 1608.
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Marco Wurster, Marius Michel, Marvin Carl May, Andreas Kuhnle, Nicole Stricker & Gisela Lanza. (2022) Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning. Journal of Intelligent Manufacturing 33:2, pages 575-591.
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Aydin Nassehi(1)(1), Marcello Colledani(1)(1), Botond Kádár(1)(1) & Eric Lutters(1)(1). (2022) Daydreaming factories. CIRP Annals 71:2, pages 671-692.
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