AIAM: Adaptive interactive attention model for solving p-Median problem via deep reinforcement learning
文献类型：期刊论文
| 作者 | Liang, Haojian6; Wang, Shaohua5,6; Li, Huilai3,4; Pan, Jie6; Li, Xiao2,6; Su, Cheng5,6; Liu, Bingzhi1 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 138页码:104454 |
| 关键词 | PMP Deep reinforcement learning Adaptive Interactive Attention Model Encoder-decoder |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2025.104454 |
| 产权排序 | 6 |
| 文献子类 | Article |
| 英文摘要 | The p-Median Problem (PMP) is a classical discrete facility location problem with significant implications for optimizing the placement of urban public service facilities. Improved heuristics, a well-established method for solving the PMP, aim to iteratively enhance solution quality through efficient neighborhood exploration. In this study, we model the neighborhood exploration process as a Markov decision process and propose a novel deep reinforcement learning approach to solving the PMP, achieving higher problem-solving efficiency and quality. The proposed method introduces an encoder-decoder structure, consisting of an Interactive Attention Encoder (IAE), a Node Removal Decoder (NRD), and a Node Insertion Decoder (NID), aimed at learning an optimal strategy for node selection. The experimental results demonstrate that our approach outperforms genetic algorithms in terms of both accuracy and computational efficiency. While the solution time is slightly longer than that of the Attention Model (AM), our method achieves a reduced gap to the optimal solution. Furthermore, ablation studies confirm that the proposed adaptive interactive encoder and the two decoders significantly enhance the model performance. Finally, we applied the Adaptive Interactive Attention Model (AIAM) to a realworld scenario, demonstrating its practical utility in guiding medical facility location decisions. |
| URL标识 | 查看原文 |
| WOS关键词 | SPATIAL OPTIMIZATION ; LOCATION ; FRAMEWORK ; NETWORKS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001441175200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213236]  |
| 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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 10094, Peoples R China 2.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China; 3.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Peoples R China; 4.Jilin Univ, Sch Math, Changchun 130012, Peoples R China; 5.Univ Chinese Acad Sci, Beijing 101408, Peoples R China; 6.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, Beijing 100094, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liang, Haojian,Wang, Shaohua,Li, Huilai,et al. AIAM: Adaptive interactive attention model for solving p-Median problem via deep reinforcement learning[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,138:104454. |
| APA | Liang, Haojian.,Wang, Shaohua.,Li, Huilai.,Pan, Jie.,Li, Xiao.,...&Liu, Bingzhi.(2025).AIAM: Adaptive interactive attention model for solving p-Median problem via deep reinforcement learning.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,138,104454. |
| MLA | Liang, Haojian,et al."AIAM: Adaptive interactive attention model for solving p-Median problem via deep reinforcement learning".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 138(2025):104454. |
入库方式： OAI收割
来源：地理科学与资源研究所
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