耦合改进图注意力网络与深度强化学习的公共服务设施智能化选址方法

An Intelligent Site Selection Approach for Public Service Facilities Coupled with Improved Graph Attention Network and Deep Reinforcement Learning

在当今城市化快速发展的背景下,公共服务设施的合理选址对于提升城市居民生活质量和提供高效服务至关重要。然而,现有常用的设施选址方法往往未能满足复杂及大规模的现实场景中对于其性能及效率上需求。为弥补这些不足,本文旨在提出一种耦合设施选址图注意力网络(Facility Location Allocation Graph Attention Network, FLA-GAT)和深度强化学习(Deep Reinforcement Learning, DRL)算法的通用图强化选址模型(Graph-Deep-Reinforcement-Learning Facility Location Allocation Model, GDRL-FLAM),通过图表示和REINFORCE算法协同解决公共服务设施智能化选址问题。为了验证提出模型的性能及效率,研究在随机生成的20、50和100个点的数据集上进行训练,并完成了相应的测试实验,结果表明:(1)在20、50和100个点的测试实例上,GDRL-FLAM模型相较于遗传算法(Genetic Algorithm, GA)性能上提升了11.79%到14.49%;在150和200个点的测试实例上,提升了1.52%到9.35%。并且随着训练集规模的增大,模型在大规模数据集上表现出更强的泛化能力;(2) GDRL-FLAM模型可以在简单场景中掌握选址策略,并使其适应到更复杂的场景,展示出了其优异的迁移学习能力;(3)在新加坡的案例研究中,GDRL-FLAM模型相较于GA在性能表现上提升了1.01%到10.75%;(4)在所有的测试及实验中,GDRL-FLAM模型在效率方面相较于GA都展示出了成倍的提升。总的来说,本研究揭示了GDRL-FLAM模型在公共服务设施选址问题上的潜在应用价值,尤其是其泛化能力及迁移学习能力为未来设施选址问题的高效解决提供了新的思路和方法。此外,该模型经过微调也可以适用于不同的空间优化问题中。最后,本研究探讨了该模型的不足之处以及下一步的研究方向。

In the context of the rapid development of urbanization,the reasonable selection of locations for public service facilities is critical for delivering efficient services and enhancing the quality of urban residents'lives.However,prevailing approaches for allocation of public service facilities often fall short of meeting the demands on their performance and efficiency in complex and large-scale real-world scenarios.To address these issues,this article proposed a novel Graph-Deep-Reinforcement-Learning Facility Location Allocation Model(GDRL-FLAM),coupling a Facility Location Allocation Graph Attention Network(FLA-GAT)with a Deep Reinforcement Learning(DRL)algorithm.This proposed model tackled the location allocation problem for public service facilities based on graph representation and the REINFORCE algorithm.To assess the performance and efficiency of the proposed model,this study conducted experiments based on randomly generated datasets with 20,50,and 100 points.The experimental results indicated that:(1)For the tests with 20,50,and 100 points,the GDRL-FLAM model exhibited a significant improvement ranging from 11.79%to 14.49%compared to the Genetic Algorithm(GA)which is one of the commonly used heuristic algorithms for addressing location allocation problems.For the tests with 150 and 200 points,the improvement ranged from 1.52%to 9.35%.Moreover,with the increase in the size of the training set,the model also demonstrated enhanced generalizability on large-scale datasets;(2)The GDRL-FLAM model showed strong transfer learning ability to obtain the location allocation strategies in simple scenarios and adapt them to more complex scenarios;(3)In the case study of Singapore,the GDRL-FLAM model outperformed GA significantly,achieving obvious improvements ranging from 1.01%to 10.75%;(4)In all these abovementioned tests and experiments,the GDRL-FLAM model showed substantial improvement in efficiency compared to GA.In short,this study demonstrated the potential of the proposed GDRL-FLAM model in addressing the location allocation issues for public service facilities,due to its generalization and transfer learning abilities.The proposed GDRL-FLAM could also be adapted to solve other spatial optimization problems.Finally,the article discussed the limitations of the model and outlined potential directions for future research.