A review on learning to solve combinatorial optimisation problems in manufacturing

• Published in: IET collaborative intelligent manufacturing Vol. 5; no. 1
• Main Authors: Zhang, Cong, Wu, Yaoxin, Ma, Yining, Song, Wen, Le, Zhang, Cao, Zhiguang, Zhang, Jie
• Format: Journal Article
• Language: English
• Published: Wuhan John Wiley & Sons, Inc 01.03.2023; Wiley
• Subjects: 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
• DOI: 10.1049/cim2.12072

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.