城市无人机站的位置优化基于MILP和机器学习的混合算法
Weijun Pan1, Jianwei Gao1, Xuan Wang1
1Civil Aviation Flight University of China, No. 46, Section 4, Nanchang Road, Guanghan, 618307, Sichuan, China.
Heliyon
|July 18, 2024
概括
利用无人机站优化城市的紧急服务,大大缩短了响应时间. 这项研究引入了一种混合方法,将聚类,优化和机器学习结合起来,以实现更好的城市应急准备.
科学领域:
- 运营研究 运营研究
- 城市规划 城市规划
- 应急管理 应急管理
背景情况:
- 由于人口密度和基础设施高,城市地区面临着火灾和事故等重大紧急情况.
- 紧急设施的分配效率低下导致反应时间延长.
- 在成都,现有的应急服务地点被认为是不理想的.
研究的目的:
- 确定无人机消防站和无人机救护中心的最佳位置和号码.
- 改善城市环境中的应急响应时间.
- 开发一个强大的框架来定位和分配紧急服务设施.
主要方法:
- 使用两阶段的集群方法 (X-means和K-means) 来确定最佳的设施配置.
- 一个混合整数线性编程 (MILP) 模型使用Gurobi平台开发和解决.
- 用贝叶斯优化 (机器学习) 来分析响应速度和服务容量.
主要成果:
- 该研究发现了当前应急设施分配的低效率,导致反应时间不足于最佳.
- 混合MILP和机器学习方法为设施定位和分配提供了强大的解决方案.
- 优化的布局显示了在城市环境中加强应急准备和更快的响应的潜力.
结论:
- 整合混合整数线性编程和机器学习为复杂的紧急服务设施问题提供了强大的框架.
- 拟议的混合算法显著提高了城市应急准备和响应能力.
- 使用无人机 (UAV) 站是改善紧急服务提供的一种可行的策略.
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