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智能交通管理系统在急救医疗站选址和救护车配置中的应用。

The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances.

机构信息

Department of Industrial Management, University of Tehran, Tehran, Iran.

Department of Business Management, Payame Noor University, Varamin Branch, Tehran, Iran.

出版信息

Comput Intell Neurosci. 2022 Jul 7;2022:2340856. doi: 10.1155/2022/2340856. eCollection 2022.

Abstract

In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the required number of ambulances to be allocated to the station. The objective functions are the maximization of covering the emergency demands and minimization of total costs. Moreover, the use of an intelligent traffic management system to speed up the ambulance is addressed. In this regard, the proposed two-objective mathematical model has been formulated, and a robust counterpart formulation under uncertainty is applied. In the proposed method, the values of the objective function increase as the problem becomes wider and, with a slight difference in large dimensions, converge in terms of the solution. The numerical results indicate that, as the problem's complexity increases, the robust optimization method is still effective because, with the increasing complexity of the problem, it can still solve large-scale problems in a reasonable time. Moreover, the difference between the value of the objective function in the proposed method and the presence of uncertainty parameters is very small and, in large dimensions, is quite logical and negligible. The sensitivity analysis shows that, with increasing demand, both the number of ambulances required and the amount of objective function have increased significantly.

摘要

在本研究中,关注的是考虑智能交通管理作用的医疗服务优化。为此,评估了医疗急救系统的双目标数学模型,以确定急救站的位置,并确定分配给该站的救护车数量。目标函数是最大化紧急需求的覆盖范围和最小化总成本。此外,还解决了使用智能交通管理系统来加快救护车速度的问题。在这方面,提出了一个双目标数学模型,并应用了不确定条件下的稳健对应模型。在所提出的方法中,随着问题的扩大,目标函数的值会增加,并且在大尺寸下,解会收敛。数值结果表明,随着问题复杂性的增加,稳健优化方法仍然有效,因为随着问题复杂性的增加,它仍然可以在合理的时间内解决大规模问题。此外,所提出方法中的目标函数值与存在不确定性参数之间的差异非常小,在大尺寸下,差异是合理且可以忽略的。敏感性分析表明,随着需求的增加,所需的救护车数量和目标函数的值都显著增加。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2c9/9283018/72e88e7967b3/CIN2022-2340856.001.jpg

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