Citations (27)
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Leonard Overbeck, Stephen C. Graves & Gisela Lanza. (2024) Development and analysis of digital twins of production systems. International Journal of Production Research 62:10, pages 3544-3558.
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Sungbum Jun & Seokcheon Lee. (2021) Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction. International Journal of Production Research 59:9, pages 2838-2856.
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Andrew Greasley & John Steven Edwards. (2021) Enhancing discrete-event simulation with big data analytics: A review. Journal of the Operational Research Society 72:2, pages 247-267.
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Articles from other publishers (24)
Anna Procopio, Giuseppe Cesarelli, Leandro Donisi, Alessio Merola, Francesco Amato & Carlo Cosentino. (2023) Combined mechanistic modeling and machine-learning approaches in systems biology – A systematic literature review. Computer Methods and Programs in Biomedicine 240, pages 107681.
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Jan-Phillip Herrmann, Sven Tackenberg & Verena Nitsch. (2023) Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access 11, pages 141172-141191.
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Andrew Greasley, Gajanan Panchal & Avinash Samvedi. (2022) The Use of Simulation with Machine Learning and Optimization for a Digital Twin-A Case on Formula 1 DSS. The Use of Simulation with Machine Learning and Optimization for a Digital Twin-A Case on Formula 1 DSS.
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Zheng Song & Shu Luo. (2022) Application of machine learning and data mining in manufacturing industry. Frontiers in Computing and Intelligent Systems 2:1, pages 47-53.
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Felipe Peña-Graf, Javier Órdenes, Ryan Wilson & Alessandro Navarra. (2022) Discrete Event Simulation for Machine-Learning Enabled Mine Production Control with Application to Gold Processing. Metals 12:2, pages 225.
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Lea Grahn, Jonas Rachner, Amon Göppert, Sazvan Saeed & Robert H. Schmitt. 2022. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems
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Zhen Qin, Yu-Ting Wu, Amre Eizad, Sung-Ki Lyu & Choon-Man Lee. (2021) Advancement of Mechanical Engineering in Extreme Environments. International Journal of Precision Engineering and Manufacturing-Green Technology 8:6, pages 1767-1782.
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Liping Zhang, Yifan Hu, Qiuhua Tang, Jie Li & Zhixiong Li. (2021) Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty. Sensors 21:14, pages 4836.
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Satoshi NAGAHARA. (2021) Modeling of job sequencing rule on shop-floor by machine learning techniques機械学習を用いた生産現場における着工順序ルールのモデリング. Transactions of the JSME (in Japanese) 87:897, pages 20-00396-20-00396.
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Leonard Overbeck, Oliver Brützel, Michael Teufel, Nicole Stricker, Andreas Kuhnle & Gisela Lanza. (2021) Continuous adaption through real data analysis turn simulation models into digital twins. Procedia CIRP 104, pages 98-103.
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James F. Leathrum, Andrew J. Collins, T. Steven Cotter, Christopher J. Lynch & Ross Gore. (2020) Education in Analytics Needed for the Modeling & Simulation Process. Education in Analytics Needed for the Modeling & Simulation Process.
Benjamin Woerrlein & Steffen Strassburger. (2020) A Method for Predicting High-Resolution Time Series Using Sequence-to-Sequence Models. A Method for Predicting High-Resolution Time Series Using Sequence-to-Sequence Models.
Alican Dogan & Derya Birant. (2020) Machine Learning and Data Mining in Manufacturing. Expert Systems with Applications, pages 114060.
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Satie L. Takeda-Berger, Enzo Morosini Frazzon, Eike Broda & Michael Freitag. 2020. Dynamics in Logistics. Dynamics in Logistics
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David Bell, Derek Groen, Navonil Mustafee, Jonathan Ozik & Steffem Strassburger. (2019) Hybrid Simulation Development – Is It Just Analytics?. Hybrid Simulation Development – Is It Just Analytics?.
Mahmoud Elbattah. (2019) How can Machine Learning Support the Practice of Modeling and Simulation? —A Review and Directions for Future Research. How can Machine Learning Support the Practice of Modeling and Simulation? —A Review and Directions for Future Research.
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Durk-Jouke van der Zee. (2019) Model simplification in manufacturing simulation – Review and framework. Computers & Industrial Engineering 127, pages 1056-1067.
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Rodrigo De la Fuente, Ignacio Erazo & Raymond L. Smith. (2018) ENABLING INTELLIGENT PROCESSES IN SIMULATION UTILIZING THE TENSORFLOW DEEP LEARNING RESOURCES. ENABLING INTELLIGENT PROCESSES IN SIMULATION UTILIZING THE TENSORFLOW DEEP LEARNING RESOURCES.
Dong-Hyeon Kim, Thomas J. Y. Kim, Xinlin Wang, Mincheol Kim, Ying-Jun Quan, Jin Woo Oh, Soo-Hong Min, Hyungjung Kim, Binayak Bhandari, Insoon Yang & Sung-Hoon Ahn. (2018) Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry. International Journal of Precision Engineering and Manufacturing-Green Technology 5:4, pages 555-568.
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