摘要
在合理假设的基础上,以使在整个时间范围内,所有需求点、所有物品延迟满足而引起的损失最小为目标,构建了应急物流车辆调度的改进数学模型,根据应急物流车辆调度的特殊情况,设计了适合求解应急货物运输调度模型的粒子群算法,然后通过一个算例对所建立的模型和算法进行了数值模拟,最后比较分析了模型改进前后的实验结果数据,并与随机搜索算法和穷举法进行了比较,证明了该模型及其算法的有效性。
Based on reasonable assumptions, the paper established an improved mathematical model of emergency logistics vehicle dispatching to minimize the loss caused by the delay of all demand points and cargoes at all times. Next, according to the special situation of the dispatching of emergency logistics vehicles, it designed the particle swarm optimization (PSO) algorithm to solve the model, and carried out a numerical example to simulate the model established and the algorithm designed. Finally, by comparing and analyzing the simulation data before and after the improvement of the model, and comparing them with that from the stochastic search algorithm and the exhaustive method, it proved the validity of the model and algorithm.
作者
袁世军
梁瑞伟
Yuan Shijun;Liang Ruiwei(Hunan Vocational College of Modern Logistics,Changsha 410131,China)
出处
《物流技术》
2019年第7期89-95,共7页
Logistics Technology
基金
2018年度湖南省社科基金项目“基于乡村振兴战略的湖南省乡村物流体系构建研究”
湖南省社会科学成果评审委员会课题“基于乡村振兴战略的乡村物流发展对策体系研究”(XSP19YBC277)
关键词
应急物流
应急货物
车辆调度
粒子群算法
emergency logistics
emergency cargo
vehicle dispatching
particle swarm optimization
