학술논문
Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review
Document Type
Review Article
Author
Source
In Journal of Manufacturing Systems December 2024 77:962-989
Subject
Language
ISSN
0278-6125
Abstract
Highlights •Systematic review of dynamic scheduling using deep reinforcement learning (DRL) for resilience and sustainability.•Key application scenarios of DRL-based dynamic scheduling across various shop floor types.•Contributions of DRL algorithms in enhancing system resilience and sustainability.•Discussion of main technical challenges in achieving resilience and sustainability with DRL.•Proposal of future research directions to address identified challenges.