A New Approach for Solving Location Routing Problems with Deep Reinforcement Learning of Emergency Medical Facility-Reference-Cited by-同舟云学术

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A New Approach for Solving Location Routing Problems with Deep Reinforcement Learning of Emergency Medical Facility

Published:2023-11-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS 2023
language:
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Author:

Wang Shaohua¹^ORCID,Zhou Junyuan²^ORCID,Liang Haojian³^ORCID,Wang Zhenbo⁴^ORCID,Su Cheng¹^ORCID,Li Xiao²^ORCID

Affiliation:

1. Aerospace Information Research Institute, CAS Beijing, China

2. Lanzhou Jiaotong University, Lanzhou, China

3. School of Artificial Intelligence, Jilin University, Changchun, China

4. Institute of Geographic Sciences and Natural Resources Research, CAS Beijing, China

Funder

the innovation group project of the Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615884.3629429

Reference17 articles.

1. Applying and Integrating Urban Contamination Factors into Community Garden Siting

2. A Spatial Decision Support System Based on a Hybrid AHP and TOPSIS Method for Fire Station Site Selection

3. Solving the p-median problem on regular and lattice networks

4. A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

5. Wang S , Liang H , Zhong Y , DeepMCLP: Solving the MCLP with Deep Reinforcement Learning for Urban Facility Location Analytics[J] . 2023 . https://doi.org/10.25436/E2KK5V 10.25436/E2KK5V Wang S, Liang H, Zhong Y, et al. DeepMCLP: Solving the MCLP with Deep Reinforcement Learning for Urban Facility Location Analytics[J]. 2023. https://doi.org/10.25436/E2KK5V

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