Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2023

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.

Keywords

bin packing, combinatorial optimisation, deep reinforcement learning, job shop scheduling, manufacturing systems, vehicle routing

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

IET Collaborative Intelligent Manufacturing

Volume

5

Issue

1

First Page

1

Last Page

24

Identifier

10.1049/cim2.12072

Publisher

Wiley Open Access

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1049/cim2.12072

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