路段通行时间不确定下取送货车辆路径优化研究

Study on the Routing Optimization of Pickup & Delivery Vehicles under Road Travel Time Uncertainty

城市高峰期交通拥堵呈常态化趋势,各运输路段的通行时间很难用精确值表达,而配送车辆的路径仍需具体指定,亟需研究路段通行时间不确定情况下的物流配送车辆的具体运输路径。文章统筹考虑配送时间与环境成本,结合Bertsimas鲁棒离散优化理论,以总行程时间和碳排放量最小为优化目标,构建了路段时间阻抗不确定情况下的同时取送货多配送中心多车路径鲁棒优化模型,采用遗传算法进行求解,并以呼和浩特市部分路网为例进行实证研究。研究结果表明:车辆路径鲁棒优化模型及其改进的遗传算法能快速生成鲁棒性能较好的车辆行驶路径,而且得到的是一条条具体的运输路径,而非配送顺序。该模型与算法能为物流配送车辆路径导航系统提供技术支撑。

Traffic congestion in peak hours shows the normalization trend, and the travel time of each transport section is difficult to express by precise value, while the path of distribution vehicle needs to be specified ,which urgently needs the study on the specific transportation path of logistics distribution vehicles under uncertain travel time.Overall considering the delivery time and environmental cost, combined with Berksimas Robust discrete optimization theory,and with the minimization of total travel time and carbon emission as the optimization target,this article established the multi-vehicle path Ro- bust optimization model of multiple delivery center with pickup and delivery at the same time under un- certain road time impedance,which was then solved by genetic algorithm,with the empirical research by taking some road network in Hohhot as the example.The research results showed that the vehicle path Robust optimization model and its improved genetic algorithm can quickly generate a vehicle travel path with better Robust performance, and obtained a series of specific transport paths rather than the distribution sequence.This model and algorithm can provide technical support for the logistics vehicle path navigation system.