基于客户点多重特性的车辆路线优化

Optimization of Vehicle Routing Problem Based on Multiple Customer Characteristics

针对传统车辆路线优化研究在对客户点商品需求特性方面存在的不足,提出了先基于客户点多重特性进行聚类分析后进行线路优化的思想.首先,将语言变量值用梯形模糊数表示,对客户点和二级准则指标进行综合评价;其次,采用模糊集成方法将二级准则指标集成到一级准则指标上,将集成后的一级指标属性值拆分为4个分属性值参与聚类算法计算,并通过设计的聚类有效性指标选取合理的聚类结果;然后,应用模糊TOPSIS方法计算各类内的客户点优先级权重;最后,构建了客户点被选择服务的评价函数式,并与动态规划方法结合进行线路优化.文中还通过实例对所提方法的有效性进行了验证,并与现有方法进行了对比.结果表明,文中方法优于单纯以距离和客户点优先级权重为测度单位的方法,线路优化结果合理,并能应用到存在大规模客户点的车辆路线优化问题中.

In order to overcome the shortcomings of the traditional vehicle routing optimization study in terms of customers＆#39; commodity demand characteristics,a clustering analysis-based routing optimization using multiple cus-tomer characteristics is proposed.In the investigation,first,linguistic variables are represented by trapezoidal fuzzy number to implement a comprehensive evaluation of both customers and sub-criterion indices.Next,the sub-criteri-on indices are integrated into a major criterion index via fuzzy integration,and the integrated major criterion value is split into four sub-criterion values for clustering operation,with a clustering validity index being designed to choose reasonable clustering results.Then,the fuzzy TOPSIS method is used to calculate the customer priority weights for each cluster.Moreover,evaluation functions for selected customer services are established and are combined with the dynamic programming method for vehicle routing optimization.Finally,the effectiveness of the proposed method is verified through an example,and is compared with the existing methods.The results show that the proposed method is superior to the method only based on distance measure or customer priority weights,and that it helps to obtain reasonable vehicle routing even in the presence of large-scale customers.