Study on Location-Routing Problem of Earthquake Relief Troops in Uncertain Unvironment

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Study on Location-Routing Problem of Earthquake Relief Troops in Uncertain Unvironment

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 05 Page number: 44-49 Column: Public date: 2021-09-10

Title:: Study on Location-Routing Problem of Earthquake Relief Troops in Uncertain Unvironment

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;

Keywords:: location-routing problem; genetic algorithm; optimization; stochastic programming; time window

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.05.015

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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Last Update: 2021-10-11