[1]黄茜,王书勤,邓少鸿,等.不确定环境下救灾部队驻地选址及搜救路径优化[J].郑州大学学报(工学版),2021,42(05):44-49.[doi:10.13705/j.issn.1671-6833.2021.05.015]
　Huang Qian,Wang Shuqin,Deng Shaohong,et al.Study on Location-Routing Problem of Earthquake Relief Troops in Uncertain Unvironment[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):44-49.[doi:10.13705/j.issn.1671-6833.2021.05.015]
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不确定环境下救灾部队驻地选址及搜救路径优化()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年05期
页码:
44-49
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
Study on Location-Routing Problem of Earthquake Relief Troops in Uncertain Unvironment
作者:
黄茜; 王书勤; 邓少鸿; 范林军
武警警官学院基础部;武警警官学院分队指挥系;长沙理工大学经济与管理学院;武警警官学院部队管理系;
Author(s):
Huang Qian; Wang Shuqin; Deng Shaohong; Fan Linjun;
Department of Basic Department of the Armed Police Police School; Department of Command Department of the Armed Police Police School; School of Economics and Management of Changsha University of Technology; Department of Economics and Management;
关键词:
Keywords:
location-routing problem;  genetic algorithm;  optimization;  stochastic programming;  time window
DOI:
10.13705/j.issn.1671-6833.2021.05.015
文献标志码:
A
摘要:
不确定因素经常影响部队救灾行动和效果，为找到不确定环境下部队驻地选址和救灾分队搜救路线规划方案，本文在假设部队行进时间和受灾点所需救灾时间均服从正态分布的基础上，建立了以救灾总成本和总时间最小的LRP多目标随机规划模型，并设计改进遗传算法求解，算例验证了求解方法的有效性。
Abstract:
Uncertain factors often affect the rescue operations and effects of troops. In the conditions of limited resources and urgent time, it is very important to select the locations of the troops, allocate the tasks of disaster relief, plan the rescue routes, organize efficient rescue, and achieve the overall optimal effect of disaster relief, overcoming the influences of uncertainty. Assuming that the time of troops′ movement and the time required for disaster relief are all in normal distribution, a multi-objective stochastic programming model of location routing problem (LRP) with the minimum total cost and time of disaster relief is established. The random constraints are transformed into the objective function by introducing the penalty factors. The normalized sum of each objective function value is taken as the fitness function value. Based on this, an improved genetic algorithm is proposed. The experimental results show that the total rescue time of the improved genetic algorithm is shorter than the one of basic genetic algorithm, and the improved ant colony algorithm has shorter total relief time and lower disaster relief cost, but the penalty value is very big, which verifies the superiority of the improved genetic algorithm

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更新日期/Last Update: 2021-10-11