摘要
研究共享汽车系统中站点供车调度问题。在浮动共享汽车系统中单日调度因数据滞后带来较大误差,改为短时间窗、多次调度。使用马尔可夫链模型进行短时预测站点内可用车辆数,再使用遗传算法,基于预测而得的站点可用车辆与需求,以满足站点需求提高用户使用率为目标进行优化,得出调度方案。在仿真系统中进行方案模拟,对调度方法的计算力,优化表现等进行评估,论证该方法的有效性与适应性。
This paper researchs the vehicle relocation scheduling problem.For large error due to data lag in full-day scheduling in free-floating car sharing system,and does short-term relocation based on Markov Chain Model to forecast available cars in each station and Genetic Algorithm to make schedule.This forecasting method meets site demand and improve user satisfaction as an optimization goal.The simulation results and evaluation of computational power and optimization show the effectiveness and stability of the method.
出处
《工业控制计算机》
2019年第5期127-128,131,共3页
Industrial Control Computer
关键词
共享汽车
调度
马尔可夫链
遗传算法
sharing car
scheduling
Markov chain
genetic algorithm
