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...

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Veröffentlicht in:IET collaborative intelligent manufacturing Jg. 5; H. 1
Hauptverfasser: Zhang, Cong, Wu, Yaoxin, Ma, Yining, Song, Wen, Le, Zhang, Cao, Zhiguang, Zhang, Jie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Wuhan John Wiley & Sons, Inc 01.03.2023
Wiley
Schlagworte:
Algorithms
Artificial intelligence
bin packing
Combinatorial analysis
combinatorial optimisation
Deep learning
deep reinforcement learning
Economic development
Graph representations
Heuristic
Industrial development
Integer programming
job shop scheduling
Job shops
Machine learning
Manufacturing
manufacturing systems
Neural networks
Production scheduling
Scheduling
vehicle routing
ISSN:2516-8398, 2516-8398
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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.
AbstractList 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.
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.
Author Zhang, Cong
Cao, Zhiguang
Ma, Yining
Le, Zhang
Wu, Yaoxin
Song, Wen
Zhang, Jie
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  fullname: Zhang, Cong
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  surname: Wu
  fullname: Wu, Yaoxin
  organization: Nanyang Technological University
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  givenname: Yining
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  surname: Ma
  fullname: Ma, Yining
  organization: National University of Singapore
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  givenname: Wen
  surname: Song
  fullname: Song, Wen
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  organization: Shandong University
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  organization: University of Electronic Science and Technology of China (UESTC)
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  surname: Zhang
  fullname: Zhang, Jie
  organization: Nanyang Technological University
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Snippet 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...
Abstract An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress...
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SubjectTerms Algorithms
Artificial intelligence
bin packing
Combinatorial analysis
combinatorial optimisation
Deep learning
deep reinforcement learning
Economic development
Graph representations
Heuristic
Industrial development
Integer programming
job shop scheduling
Job shops
Machine learning
Manufacturing
manufacturing systems
Neural networks
Production scheduling
Scheduling
vehicle routing
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Title A review on learning to solve combinatorial optimisation problems in manufacturing
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