摘要： 针对动态车辆路径调度问题的难解性,提出一种基于大数据计算模型的车辆路径调度算法。利用大数据计算收集和处理初始数据,在关键路径节点设置警报标志实现动态更新路径机制,将传统的单一时间成本车辆路径问题描述为动态时间序列的运输成本优化问题,并给出运输总成本的目标函数。在Sioux Falls网络中进行的数值模拟分析结果表明,该算法能有效地求解车辆路径调度问题,且随着网络数据量的动态性增加,与传统的PFIH算法、Tabu算法相比,其时间性能具有明显优势。

关键词: 大数据, 动态车辆路由, 优化问题, 目标函数, 数值模拟

Abstract: In order to solve some problem of dynamic vehicle routing,this paper proposes a vehicle routing algorithm based on big-data calculation model.This algorithm uses big-data calculation to collect and process the initial data,it sets up the alerts trigger in key routing nodes to implement routing mechanism update dynamically,and transforms traditional single time cost vehicle routing problem into transportation cost optimization problem based on dynamic time sequence,and gives the total transportation cost of objective function.Numerical simulation results with the Sioux Falls network show this algorithm is quite effective for vehicle routing problem,compared with the traditional PFIH algorithm and Tabu algorithm,the time performance has obvious advantages.

Key words: big-data, dynamic vehicle routing, optimization problem, objective function, numerical simulation

中图分类号: 

• TP391