Real-time production scheduling using a deep reinforcement learning-based multi-agent approach-Reference-Cited by-同舟云学术

Real-time production scheduling using a deep reinforcement learning-based multi-agent approach

Published:2024-03-03 Issue:2 Volume:62 Page:186-210
ISSN:0315-5986
Container-title:INFOR: Information Systems and Operational Research
language:en
Short-container-title:INFOR: Information Systems and Operational Research

Author:

Taghipour Sharareh¹,Namoura Hamed A.¹,Sharifi Mani²,Ghaleb Mageed¹

Affiliation:

1. The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada

2. Department of Information Systems & Analytics, Farmer School of Business, Miami University, Oxford, OH, USA

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Foundation for Innovation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03155986.2023.2287996

Reference62 articles.

1. Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

2. Developing Real-Time Scheduling Policy by Deep Reinforcement Learning

3. Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey

4. Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production

5. Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line

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