摘要
针对服务质量评价中指标权重确定的问题,提出一种基于粗糙集与粒子群算法的权重优化模型。首先,利用粗糙集与模糊集,计算出初步权重并给出各个指标权重变化的区间,且在各区间内生成不同的权重值。再通过仿真实验来生成针对不同指标权重所产生的评价结果,定量计算出评价结果的方差。然后通过方差作为粒子群算法的适应度函数进行后向反馈,实现指标权重的优化。最后,将优化后的权重用于物流企业物流服务质量评价结果的计算。
In terms of the problem of index weight determination in service quality evaluation,the paper proposed a weight optimization model based on rough set theory and particle swarm optimization.Firstly,it combines the rough set theory and the fuzzy set theory to calculate the initial weight and give the interval of each index weight change,and generate different weight values in each interval.And through simulation experiments to generate evaluation results for different index weights,and quantitatively calculate the variance of the evaluation results.Then the variance is used as the fitness function of the particle swarm algorithm to perform backward feedback to optimize the index weights.Finally,the optimized weights are used in the calculation of the evaluation results of the logistics service quality of the logistics enterprises.
作者
邵俊杰
SHAO Jun-jie(School of Business Jiangnan University,Wuxi 214122,China)
出处
《经济研究导刊》
2022年第13期43-47,共5页
Economic Research Guide
关键词
物流服务
粗糙集理论
权重优化
粒子群算法
logistics service
rough set theory
weight optimization
particle swarm optimization
