A Deep Reinforcement Learning-based Approach to Post-Disaster Routing of Movable Energy Resources-Reference-Cited by-同舟云学术

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A Deep Reinforcement Learning-based Approach to Post-Disaster Routing of Movable Energy Resources

Published:2022-10-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Industry Applications Society Annual Meeting (IAS)
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Author:

Gautam Mukesh¹,Bhusal Narayan¹,Benidris Mohammed¹

Affiliation:

1. University of Nevada,Department of Electrical & Biomedical Engineering,Reno

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9939685/9939671/09940073.pdf?arnumber=9940073

Reference17 articles.

1. Formulations and branch-and-cut algorithm for the k-rooted mini-max spanning for-est problem;salles;International Network Optimization Conference,2011

2. Solution of minimum spanning forest problems with reliability constraints

3. An approach to parallelize kruskal's algorithm using helper threads;katsigiannis;2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum,2012

4. Distribution Network Reconfiguration Using Deep Reinforcement Learning

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach;IEEE Industry Applications Magazine;2024-03

2. Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis;Electronics;2023-10-14

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