基于混合遗传算法的紧急程度不确定应急物流问题求解

张玉州,徐廷政,郑军帅

系统科学与数学 ›› 2020, Vol. 40 ›› Issue (4) : 714-728.

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PDF(1135 KB)
系统科学与数学 ›› 2020, Vol. 40 ›› Issue (4) : 714-728. DOI: 10.12341/jssms13847
论文

基于混合遗传算法的紧急程度不确定应急物流问题求解

    张玉州,徐廷政,郑军帅
作者信息 +

Solving Emergency Logistics Problem with Uncertain Urgency\ Based on a Hybrid Genetic Algorithm

    ZHANG Yuzhou ,XU Tingzheng ,ZHENG Junshuai
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摘要

针对救灾工作中因紧急度不确定性造成物资配送延误的问题, 以总延 误时间和总运输时间为优化目标, 建立紧急程度不确定的应急物流规划模型, 以Beta分布 模拟灾区紧急度变化情况, 同时进行预测. 设计基于紧急度的混合遗传算法, 在该算法的局 部搜索阶段使用一种紧急度依赖的路径调整算法, 根据物资需求点的紧急度不同的特性, 对存 在延误的配送路径进行有针对性的优化. 实验结果表明, 所提模型和算法有效降低了延误和运输 时间, 尤其延误时间, 与一些经典算法相比改进明显, 且在多组算例中效果稳定, 具有良好的鲁棒性.

Abstract

Aiming at the problem of material distribution delay caused by uncertainty of emergency degree in disaster relief work, an emergency logistics planning model with uncertainty of emergency degree was established. In the model, the Beta distribution was used to simulate the change of emergency degree in disaster area, and the total delay time and transportation time were taken as optimization objectives. A hybrid genetic algorithm based on emergency degree is designed for the problem, and a task redistribution algorithm based on emergency degree is used in the local search phase of genetic algorithm. According to the different characteristics of emergency degree of material demand points, the distribution route with delay is optimized. The experimental results show that the proposed model and algorithm have effect in tackling the objectives. Especially, the improvement in delay time obtained by the proposed algorithm is obvious compared with some classical algorithms. The performance of the model and algorithm is stable in a number of examples and quite robust.

关键词

应急救灾 / 不确定 / 车辆路径问题 / 遗传算法 / 紧急度.

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张玉州 , 徐廷政 , 郑军帅. 基于混合遗传算法的紧急程度不确定应急物流问题求解. 系统科学与数学, 2020, 40(4): 714-728. https://doi.org/10.12341/jssms13847
ZHANG Yuzhou , XU Tingzheng , ZHENG Junshuai. Solving Emergency Logistics Problem with Uncertain Urgency\ Based on a Hybrid Genetic Algorithm. Journal of Systems Science and Mathematical Sciences, 2020, 40(4): 714-728 https://doi.org/10.12341/jssms13847
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