Review of job shop scheduling research and its new perspectives under Industry 4.0

By Jian Zhang, Guofu Ding, Yisheng Zou, S. Qin, Jianlin Fu. Journal of Intelligent Manufacturing, 2017

502 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2cc40447281b4d8604c7c7dda22c5ecb53812e73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Wen Song
2. Qiqiang Li
3. Marco Perona
4. Andrea Bacchetti
5. Ting Zheng
6. Zhiguang Cao
7. Marco Ardolino

Citations in the corpus

• Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning (2023, methodology): “Experiments show that the proposed method outperforms traditional PDRs and is computationally efficient, even on instances of larger scales and different properties unseen in training.”
• The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review (2020, methodology): “…techniques can verify the correctness and security of planning and scheduling decisions by comparing the actual job shop statement and digital twin job shop statement (Cimino, Negri, and Fumagalli 2019; Guizzi, Falcone, and De Felice 2019; Tao and Zhang 2017; Zhang, Ding, Zou, et al.2019).”