A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since t...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/173735 |
| _version_ | 1811695192475959296 |
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| author | Zhang, Cong Wu, Yaoxin Ma, Yining Song, Wen Le, Zhang Cao, Zhiguang Zhang, Jie |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Cong Wu, Yaoxin Ma, Yining Song, Wen Le, Zhang Cao, Zhiguang Zhang, Jie |
| author_sort | Zhang, Cong |
| collection | NTU |
| description | An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state-of-the-art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges. |
| first_indexed | 2024-10-01T07:19:34Z |
| format | Journal Article |
| id | ntu-10356/173735 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:19:34Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1737352024-03-01T15:36:30Z A review on learning to solve combinatorial optimisation problems in manufacturing Zhang, Cong Wu, Yaoxin Ma, Yining Song, Wen Le, Zhang Cao, Zhiguang Zhang, Jie School of Computer Science and Engineering Computer and Information Science Bin packing Combinatorial optimisation An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state-of-the-art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges. Agency for Science, Technology and Research (A*STAR) Published version This work was supported in part by the National Natural Science Foundation of China under Grant 62102228 and the Shandong Provincial Natural Science Foundation under Grant ZR2021QF063. This work was also supported by the A*STAR Cyber-Physical Production System (CPPS) – Towards Contextual and Intelligent Response Research Program, under the RIE2020 IAF-PP Grant A19C1a0018, and Model Factory@SIMTech. Finally, this work is supported in part by the A*Star Career Development Fund under Grant C222812027. 2024-02-26T04:19:30Z 2024-02-26T04:19:30Z 2023 Journal Article Zhang, C., Wu, Y., Ma, Y., Song, W., Le, Z., Cao, Z. & Zhang, J. (2023). A review on learning to solve combinatorial optimisation problems in manufacturing. IET Collaborative Intelligent Manufacturing, 5(1), e12072.-. https://dx.doi.org/10.1049/cim2.12072 2516-8398 https://hdl.handle.net/10356/173735 10.1049/cim2.12072 2-s2.0-85149487801 1 5 e12072. en A19C1a0018 C222812027 IET Collaborative Intelligent Manufacturing © 2023 The Authors. IET Collaborative Intelligent Manufacturing published by John Wiley& Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. application/pdf |
| spellingShingle | Computer and Information Science Bin packing Combinatorial optimisation Zhang, Cong Wu, Yaoxin Ma, Yining Song, Wen Le, Zhang Cao, Zhiguang Zhang, Jie A review on learning to solve combinatorial optimisation problems in manufacturing |
| title | A review on learning to solve combinatorial optimisation problems in manufacturing |
| title_full | A review on learning to solve combinatorial optimisation problems in manufacturing |
| title_fullStr | A review on learning to solve combinatorial optimisation problems in manufacturing |
| title_full_unstemmed | A review on learning to solve combinatorial optimisation problems in manufacturing |
| title_short | A review on learning to solve combinatorial optimisation problems in manufacturing |
| title_sort | review on learning to solve combinatorial optimisation problems in manufacturing |
| topic | Computer and Information Science Bin packing Combinatorial optimisation |
| url | https://hdl.handle.net/10356/173735 |
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