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Review on Learning-based Methods for shop Scheduling problems
College of Informatics, Huazhong Agricultural University, Wuhan, China.
Department of Computing and Informatics, Bournemouth University, United Kingdom.
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. (Virtual Manufacturing Processes (VMP))ORCID-id: 0000-0003-1781-2753
Faculty of Business, Computing and Digital Industries, Leeds Trinity University, United Kingdom.
2022 (Engelska)Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022: 14–16 October 2022 Bournemouth, United Kingdom, IEEE, 2022, s. 294-298Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized. 

Ort, förlag, år, upplaga, sidor
IEEE, 2022. s. 294-298
Nyckelord [en]
Learning systems, Reinforcement learning, Classifieds, Learning-based methods, Machine learning techniques, Manufacturing performance, Reinforcement learnings, Related works, Research opportunities, Scheduling problem, Shop scheduling, Shop scheduling problem, Neural networks, artificial neural networks, learning-based method, shop scheduling problems
Nationell ämneskategori
Annan elektroteknik och elektronik Produktionsteknik, arbetsvetenskap och ergonomi Kommunikationssystem
Forskningsämne
Virtual Manufacturing Processes; VF-KDO
Identifikatorer
URN: urn:nbn:se:his:diva-22313DOI: 10.1109/ICEBE55470.2022.00058Scopus ID: 2-s2.0-85148646349ISBN: 978-1-6654-9244-7 (digital)ISBN: 978-1-6654-9245-4 (tryckt)OAI: oai:DiVA.org:his-22313DiVA, id: diva2:1740759
Konferens
IEEE International Conference on E-Business Engineering (ICEBE), 14–16 October 2022 Bournemouth, United Kingdom
Ingår i projekt
Virtuella fabriker med kunskapsdriven optimering (VF-KDO), KK-stiftelsen
Anmärkning

© 2022 IEEE

The work is supported by the Knowledge Foundation (KKS), Sweden, through Virtual Factory with Knowledge-Driven Optimization (VF-KDO) project and Natural Science Foundation of China (grant no. 61803169). The paper reflects only the authors’ views.

Tillgänglig från: 2023-03-02 Skapad: 2023-03-02 Senast uppdaterad: 2023-09-08Bibliografiskt granskad

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Wang, Wei

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Institutionen för ingenjörsvetenskapForskningsmiljön Virtuell produkt- och produktionsutveckling
Annan elektroteknik och elektronikProduktionsteknik, arbetsvetenskap och ergonomiKommunikationssystem

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