大数据分析背景下地震后紧急物流资源调度模型设计
Design of a Model for the Scheduling of Emergency Logistics Resources after Earthquakes against the Background of Big Data Analysis

在地震后伤员救治和紧急物流资源配送时，传统基于轮询调度算法的调度模型存在效率低的问题。针对这一问题，设计一种大数据分析背景下地震后紧急物流资源调度模型。根据受灾点的物资需求量和伤员的救治需求，以最低损耗为目的，在车辆和直升机调度时设计地震后紧急物流资源的调度模型；在此基础上采用遗传调度算法，通过初始化种群获取模型中的多种资源调度策略，根据子代种群的适应度值计算资源调度路径的选择概率，经过交叉和变异实现高效快速的地震后紧急物流资源调度。实验结果表明，所设计模型可对震后救援资源的高效调度起到良好指导作用。
A model for the scheduling of emergency logistics resources after earthquakes was designed against the background of big data analysis to address the low efficiency of the traditional resource scheduling model, which is based on the round robin scheduling algorithm. Vehicle and helicopter schedules were established with the purpose of loss minimization and in accordance with the material demand of affected sites and the treatment demand of casualties. In the model, the genetic scheduling algorithm was used to obtain multiple resource scheduling strategies through population initialization. The selection probability of the resource scheduling path was calculated in accordance with the subpopulation fitness value. Experimental results showed that the designed model can guide the efficient dispatching of postearthquake rescue resources.