A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions
Author:
Affiliation:
1. Department of Mechanical Engineering Chair of Automation and Information Systems Technical University Munich Munich Germany
2. School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China
Funder
China Scholarship Council
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Hardware and Architecture
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/cim2.12060
Reference37 articles.
1. Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm
2. An Improved Biogeography‐Based Optimization Algorithm for Blocking Flow Shop Scheduling Problem
3. A genetic algorithm for the Flexible Job-shop Scheduling Problem
4. Multi-objective scheduling of dynamic job shop using variable neighborhood search
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3. Integration of deep reinforcement learning and multi-agent system for dynamic scheduling of re-entrant hybrid flow shop considering worker fatigue and skill levels;Robotics and Computer-Integrated Manufacturing;2023-12
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