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基于混合整数线性规划(MILP)与机器学习的城市无人机基站选址优化

Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning.

作者信息

Pan Weijun, Gao Jianwei, Wang Xuan, Zuo Qinghai, Tan Shijie

机构信息

Civil Aviation Flight University of China, No. 46, Section 4, Nanchang Road, Guanghan, 618307, Sichuan, China.

出版信息

Heliyon. 2024 Jun 17;10(12):e32928. doi: 10.1016/j.heliyon.2024.e32928. eCollection 2024 Jun 30.

Abstract

Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents. To address this challenge, a two-stage clustering method, incorporating X-means and K-means algorithms, is employed to identify optimal number and locations for Unmanned Aerial Vehicle (UAV) fire stations and drone ambulance centers. A Mixed-Integer Linear Programming (MILP) model is subsequently constructed and solved using the Gurobi optimization platform. Bayesian optimization-a machine learning technique-is exploited to elucidate the interplay between response speed and service capacity of these UAV-based emergency service stations under an optimized layout. Results affirm that integration of MILP and machine learning provides a robust framework for solving complex problems related to the siting and allocation of emergency service facilities. The proposed hybrid algorithm demonstrates substantial potential for enhancing emergency preparedness and response in urban settings.

摘要

城市环境人口密度高且基础设施复杂,容易发生火灾和交通事故等一系列紧急情况。因此,消防站和急救中心的优化布局与分布对于保障生命和财产安全至关重要。对成都部分地区应急服务设施分布低效情况的调查显示,这些设施分布不均衡导致在关键事件发生时响应时间不理想。为应对这一挑战,采用了一种结合X均值和K均值算法的两阶段聚类方法,以确定无人机消防站和无人机急救中心的最佳数量和位置。随后构建了一个混合整数线性规划(MILP)模型,并使用Gurobi优化平台进行求解。利用贝叶斯优化——一种机器学习技术——来阐明在优化布局下这些基于无人机的应急服务站的响应速度和服务能力之间的相互作用。结果证实,MILP与机器学习的结合为解决与应急服务设施选址和分配相关的复杂问题提供了一个强大的框架。所提出的混合算法在增强城市环境中的应急准备和响应方面显示出巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c0e/11252858/b0d035ecdf6b/gr001.jpg

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