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ReCovNet: Reinforcement learning with covering information for solving maximal coverage billboards location problem

文献类型：期刊论文

作者Zhong, Yang1; Wang, Shaohua2,3; Liang, Haojian4; Wang, Zhenbo5; Zhang, Xueyan6; Chen, Xi7; Su, Cheng2,3
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2024-04-01
卷号128页码:13
关键词Maximal coverage billboards location problem (MCBLP) Attention model Mixed -integer linear programming Deep reinforcement learning Spatial optimization
ISSN号1569-8432
DOI10.1016/j.jag.2024.103710
通讯作者Wang, Shaohua(wangshaohua@aircas.ac.cn)
英文摘要Maximizing billboard coverage with limited resources and different objective goals plays a vital role in social activities. The Maximal Coverage Billboard Location Problem (MCBLP) is complex, especially for multi -objective functions. A multi -objective spatial optimization model was developed using mixed -integer linear programming based on MCBLP to formulate the spatial optimization problem of determining billboard locations. Combining the distinctive features of location problems, we have developed a new approach called ReCovNet that utilizes Deep Reinforcement Learning (DRL) to solve the MCBLP. We applied the ReCovNet to address a real -world billboard location problem in New York City. To assess its performance, we implemented various algorithms such as Gurobi solver, Genetic Algorithm (GA) and a deep learning baseline called Attention Model (AM). The Gurobi reports the optimal solutions, while GA and AM serve as benchmark algorithms. Our proposed approach achieves a good balance between efficiency and accuracy and effectively solves MCBLP. The ReCovNet introduced in our study has potential to improve advertising effectiveness, and our proposed approach offers novel insights for addressing the MCBLP.
资助项目Innovation group project of the Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences[E33D0201-5] ; CBAS project 2023, Henan Zhongmu County Research Project[E3C1050101] ; Beijing Chaoyang District Collaborative Innovation Project[E2DZ050100] ; Remote Sensing Big Data Analystics Project[E3E2051401]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001198864300001
出版者ELSEVIER
资助机构Innovation group project of the Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences ; CBAS project 2023, Henan Zhongmu County Research Project ; Beijing Chaoyang District Collaborative Innovation Project ; Remote Sensing Big Data Analystics Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/204616]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Shaohua
作者单位1.Claremont Grad Univ, Sch Informat Syst & Technol, Claremont, CA 91711 USA
2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
3.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
4.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
6.Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90089 USA
7.UCL, Dept Stat Sci, London WC1E 6BT, England
推荐引用方式
GB/T 7714		 Zhong, Yang,Wang, Shaohua,Liang, Haojian,et al. ReCovNet: Reinforcement learning with covering information for solving maximal coverage billboards location problem[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,128:13.
APA Zhong, Yang.,Wang, Shaohua.,Liang, Haojian.,Wang, Zhenbo.,Zhang, Xueyan.,...&Su, Cheng.(2024).ReCovNet: Reinforcement learning with covering information for solving maximal coverage billboards location problem.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,128,13.
MLA Zhong, Yang,et al."ReCovNet: Reinforcement learning with covering information for solving maximal coverage billboards location problem".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 128(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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