Enterprise and service−level scheduling of robot production services in cloud manufacturing with deep reinforcement learning
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-023-02285-z
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Andy Ham, 2021. "Transfer-robot task scheduling in job shop," International Journal of Production Research, Taylor & Francis Journals, vol. 59(3), pages 813-823, February.
- Fei Qiao & Juan Liu & Yumin Ma, 2021. "Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7139-7159, December.
- Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
- Daniel Tonke & Martin Grunow, 2018. "Maintenance, shutdown and production scheduling in semiconductor robotic cells," International Journal of Production Research, Taylor & Francis Journals, vol. 56(9), pages 3306-3325, May.
- Alexandre Dolgui & Dmitry Ivanov & Suresh P. Sethi & Boris Sokolov, 2019. "Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 57(2), pages 411-432, January.
- Zengqiang Jiang & Shuai Yuan & Jing Ma & Qiang Wang, 2022. "The evolution of production scheduling from Industry 3.0 through Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 60(11), pages 3534-3554, June.
- Yongkui Liu & Lihui Wang & Xi Vincent Wang & Xun Xu & Lin Zhang, 2019. "Scheduling in cloud manufacturing: state-of-the-art and research challenges," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4854-4879, August.
- Miguel Vieira & Samuel Moniz & Bruno S. Gonçalves & Tânia Pinto-Varela & Ana Paula Barbosa-Póvoa & Pedro Neto, 2022. "A two-level optimisation-simulation method for production planning and scheduling: the industrial case of a human–robot collaborative assembly line," International Journal of Production Research, Taylor & Francis Journals, vol. 60(9), pages 2942-2962, May.
- Foivos Psarommatis & Gökan May & Paul-Arthur Dreyfus & Dimitris Kiritsis, 2020. "Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 1-17, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dalila B. M. M. Fontes & S. Mahdi Homayouni & Mauricio G. C. Resende, 2022. "Job-shop scheduling-joint consideration of production, transport, and storage/retrieval systems," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1284-1322, September.
- Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
- Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
- Goli, Alireza, 2024. "Efficient optimization of robust project scheduling for industry 4.0: A hybrid approach based on machine learning and meta-heuristic algorithms," International Journal of Production Economics, Elsevier, vol. 278(C).
- Metzker Soares, Paula & Thevenin, Simon & Adulyasak, Yossiri & Dolgui, Alexandre, 2024. "Adaptive robust optimization for lot-sizing under yield uncertainty," European Journal of Operational Research, Elsevier, vol. 313(2), pages 513-526.
- Dong Yang & Qidong Liu & Jia Li & Yongji Jia, 2020. "Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
- Runze Liu & Qi Jia & Hui Yu & Kaizhou Gao & Yaping Fu & Li Yin, 2025. "Bi-Objective Integrated Scheduling of Job Shop Problems and Material Handling Robots with Setup Time," Mathematics, MDPI, vol. 13(3), pages 1-33, January.
- Zhitao Xu & Adel Elomri & Roberto Baldacci & Laoucine Kerbache & Zhenyong Wu, 2024. "Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective," Annals of Operations Research, Springer, vol. 338(2), pages 1359-1401, July.
- Rosmaini Ahmad & Rabiatul Fakhira Mohd Amin & Shaliza Azreen Mustafa, 2022. "Value stream mapping with lean thinking model for effective non-value added identification, evaluation and solution processes," Operations Management Research, Springer, vol. 15(3), pages 1490-1509, December.
- Hosseini, Amir & Otto, Alena & Pesch, Erwin, 2024. "Scheduling in manufacturing with transportation: Classification and solution techniques," European Journal of Operational Research, Elsevier, vol. 315(3), pages 821-843.
- Gokan May & Foivos Psarommatis, 2023. "Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research," Energies, MDPI, vol. 16(10), pages 1-28, May.
- Wang Shijie & Zhang Yingfeng, 2021. "A credit-based dynamical evaluation method for the smart configuration of manufacturing services under Industrial Internet of Things," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1091-1115, April.
- Foivos Psarommatis & Gokan May, 2025. "A Cost–Benefit Model for Sustainable Product Reuse and Repurposing in Circular Remanufacturing," Sustainability, MDPI, vol. 17(1), pages 1-16, January.
- Usama Awan & Robert Sroufe & Muhammad Shahbaz, 2021. "Industry 4.0 and the circular economy: A literature review and recommendations for future research," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2038-2060, May.
- Siregar Martha Leni & Muskananfola Max Rudolf, 2024. "Factors Associated with Medium Trucks Casualties in the Special Region of Yogyakarta, Indonesia," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 15(1), pages 1-11.
- Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
- Ke-Liang Wang & Ting-Ting Sun & Ru-Yu Xu, 2023. "The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises," Economic Change and Restructuring, Springer, vol. 56(2), pages 1113-1146, April.
- Seokgi Lee & Hyun Woo Jeon & Mona Issabakhsh & Ahmad Ebrahimi, 2022. "An electric forklift routing problem with battery charging and energy penalty constraints," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1761-1777, August.
- R. Micale & C. M. La Fata & M. Enea & G. La Scalia, 2021. "Regenerative scheduling problem in engineer to order manufacturing: an economic assessment," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1913-1925, October.
- Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
More about this item
Keywords
Cloud manufacturing; Scheduling; Robot production service; Deep reinforcement learning; Average-DQN;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02285-z. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.