摘要
为处理属性绩效值和总体顾客满意度间的非线性关系,以及属性绩效值和属性重要度间的依赖关系,提出基于粗糙集的改进重要度绩效分析法。通过挖掘绩效值和总体顾客满意度间的数据关系,获得潜在的属性重要度。由于粗糙集无法处理连续属性,结合模糊c均值聚类算法对数据进行离散化处理。该方法克服了传统重要度绩效分析法难以准确描述绩效值、重要度和总体顾客满意度间的关系,且重要度的确定主观性强的缺陷。以水平定向钻机的维修服务为例,验证了所提方法的可行性与有效性。
To deal with the non-linear relationship between attribute performance and overall customer satisfaction,and consider the dependence relationship between attribute importance and attribute performance,an Importance Performance Analysis（IPA） method based on rough set theory was presented.Potential attribute importance was achieved by mining data relationship between attribute performance and customer satisfaction.Due to the fact that rough sets theory was unable to deal with continuous attributes,fuzzy c-means clustering algorithm was adopted to discrete data.This method overcome shortcomings of traditional IPA method,such as difficulty in describing relationships among performance,importance,overall customer satisfaction and subjectivity in determining attribute.A maintenance service of horizontal directional drilling machine was presented to demonstrate the effectiveness and feasibility of the revised method.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第7期1374-1380,共7页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51075261)
上海市科技创新行动计划资助项目(09dz1124600
10dz1121600)
上海交通大学研究生创新能力培养专项基金资助项目~~
关键词
服务属性
重要度
绩效分析
模糊C均值聚类算法
粗糙集理论
service attribute
importance degree
performance analysis
fuzzy c-means clustering algorithm
rough set theory
