A review on learning to solve combinatorial optimisation problems in manufacturing
Abstract 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. Eve...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | IET Collaborative Intelligent Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cim2.12072 |
| _version_ | 1797860814326071296 |
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| author | Cong Zhang Yaoxin Wu Yining Ma Wen Song Zhang Le Zhiguang Cao Jie Zhang |
| author_facet | Cong Zhang Yaoxin Wu Yining Ma Wen Song Zhang Le Zhiguang Cao Jie Zhang |
| author_sort | Cong Zhang |
| collection | DOAJ |
| description | Abstract 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 | 2024-04-09T21:51:57Z |
| format | Article |
| id | doaj.art-53311662ab2f4d46a0474213c65ad74b |
| institution | Directory Open Access Journal |
| issn | 2516-8398 |
| language | English |
| last_indexed | 2024-04-09T21:51:57Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj.art-53311662ab2f4d46a0474213c65ad74b2023-03-24T12:44:28ZengWileyIET Collaborative Intelligent Manufacturing2516-83982023-03-0151n/an/a10.1049/cim2.12072A review on learning to solve combinatorial optimisation problems in manufacturingCong Zhang0Yaoxin Wu1Yining Ma2Wen Song3Zhang Le4Zhiguang Cao5Jie Zhang6School of Computer Science and Engineering Nanyang Technological University Singapore SingaporeSchool of Computer Science and Engineering Nanyang Technological University Singapore SingaporeDepartment of Industrial Systems Engineering and Management National University of Singapore Singapore SingaporeInstitute of Marine Science and Technology Shandong University Qingdao ChinaSchool of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu ChinaSingapore Institute of Manufacturing Technology (SIMTech), A*STAR Singapore SingaporeSchool of Computer Science and Engineering Nanyang Technological University Singapore SingaporeAbstract 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.https://doi.org/10.1049/cim2.12072bin packingcombinatorial optimisationdeep reinforcement learningjob shop schedulingmanufacturing systemsvehicle routing |
| spellingShingle | Cong Zhang Yaoxin Wu Yining Ma Wen Song Zhang Le Zhiguang Cao Jie Zhang A review on learning to solve combinatorial optimisation problems in manufacturing IET Collaborative Intelligent Manufacturing bin packing combinatorial optimisation deep reinforcement learning job shop scheduling manufacturing systems vehicle routing |
| 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 | bin packing combinatorial optimisation deep reinforcement learning job shop scheduling manufacturing systems vehicle routing |
| url | https://doi.org/10.1049/cim2.12072 |
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