Affiliation:
1. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore
2. Department of Industrial Systems Engineering and Management National University of Singapore Singapore Singapore
3. Institute of Marine Science and Technology Shandong University Qingdao China
4. School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China
5. Singapore Institute of Manufacturing Technology (SIMTech), A*STAR Singapore Singapore
Abstract
AbstractAn 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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Hardware and Architecture
Reference195 articles.
1. Machine learning for combinatorial optimization: A methodological tour d’horizon
2. Learning Improvement Heuristics for Solving Routing Problems..
3. Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
4. Baluja S. Davies S.:Using optimal dependency‐trees for combinational optimization. In:Proceedings of the Fourteenth International Conference on Machine Learning pp.30–38(1997)
5. Moll R. et al.:Learning instance‐independent value functions to enhance local search. In:Advances in Neural Information Processing Systems pp.1017–1023(1999)
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