摘要
基于现实生活中客户有多个时间段可以接受服务及客户对服务时间要求模糊性的特征,将多个时间窗模糊化,通过服务开始时间的隶属度函数量化客户满意度,在给定一定满意度的基础上,以总成本最小及满意度最大为目标,构建了多模糊时间窗的车辆路径问题模型.根据模型特点,利用惩罚因子处理约束条件,应用粒子群算法求解模型,实验结果表明,该算法能够较好地解决多模糊时间窗的车辆路径问题,并将算例结果与原结果相比较发现,多模糊时间窗车辆路径问题模型能够更加有效地降低配送成本.
Based on the characteristics that the customers could accept service in multiple time periods and the fuzziness of service time periods, this paper deals with the multiple time windows as fuzzy variables and quantifies the customers＇ satisfaction level according to the membership function of the beginning time to be served, on the basis of the given acceptable satisfaction level, the vehicle routing model with multiple fuzzy time windows is constructed in order to minimize the total cost and maximize the satisfaction level. Then according to the model characteristics, we use the punishment factors to deal with the constraints and apply particle swarm operations to solve the proposed problems. The experimental results show that the effectiveness of proposed algorithm in solving the vehicle routing problems with multiple fuzzy time windows. Comparing the calculated results with the original results, it is found that our proposed model is more effective to reduce the cost of distribution.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2016年第6期182-188,共7页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(71401020)
系统科学与企业发展研究中心一般项目(Xq15C03)
河北省高等学校人文社会科学研究项目(BJ2016057)~~
关键词
综合交通运输
多模糊时间窗
粒子群
车辆路径
顾客满意度
integrated transportation
multiple fuzzy time windows
particle swarm operations
vehicle routing
customers＇ satisfaction
