Fuzzy Efficiency Measurement And Cross Efficiency Analysis

Since there are some uncertainty,ambiguity,and complexity data information in decision-making problems,so the evaluation analysis of fuzzy objects has attracted researchers' widespread attention.Fuzzy comprehensive evaluation is a common method to solve such problems.It can provide decision makers with comprehensive evaluation results,but it does not further provide improvement directions for the evaluated object.Data envelopment analysis(DEA)is a significant method to evaluate the relative efficiency between a set of homogeneous decision-making units(DMUs)with multiple inputs and multiple outputs.This method can not only give the effectiveness of the DMU,but also provide decision-makers with the reason and degree of ineffectiveness of the DMU.With its unique advantages,the DEA method has been widely used in economic management,and so on.Therefore,combining the two methods to evaluate fuzzy objects,and making the two methods complement to each other,which is of great significance for improving and enhancing the method's ability to solve problems.In addition,since the traditional DEA method is weak to distinguish some effective units,it cannot give a full ranking of all DMUs.Hence,it is also very meaningful to research on the efficiency ranking problem.This main work is summarized as following:(1)To strengthen the evaluation ability of the single degree fuzzy comprehensive evaluation method,a fuzzy evaluation results possible set is constructed based on fuzzy comprehensive evaluation information,the meaning of the effectiveness of fuzzy event is given,and a fuzzy effective measurement model is proposed.This method can not only find out the deficiency of the evaluated object,but also provide a lot of information for further improvement.(2)To strengthen the evaluation ability of triangular fuzzy number comprehensive evaluation,a fuzzy evaluation results possible set is constructed in combination with triangular fuzzy number evaluation,and a measure method of fuzzy effectiveness is given.The shortage of the evaluated object and the improvement range of the indicators are obtained through model analysis.(3)To strengthen the evaluation ability of the fuzzy comprehensive problems with hybrid factors,the evaluation methods of quantitative and non-quantitative factors are introduced.The fuzzy evaluation results possible set is constructed based on fuzzy comprehensive evaluation,and a measure method of fuzzy effectiveness is given.This method can not only find out the reasons for the insufficient of evaluation objects,but also obtain the improvement value of the various factors.(4)To strengthen the evaluation ability of the multi-level fuzzy comprehensive evaluation method,the differences between the multi-level fuzzy comprehensive evaluation method and DEA method are pointed out.Combining the characteristics of these two methods,some multilevel fuzzy evaluation results possible set are presented,the relevant fuzzy meaning and effectiveness measurement methods are given.The model can not only give the validity degree of the evaluated object,but also propose a new path for the improvement of fuzzy comprehensive evaluation method.(5)To rank the efficiency of all DMUs,a DEA cross-efficiency ranking method based on grey correlation and relative entropy is proposed.This method combines the characteristics of both methods to determine the relative proximity,that is,to determine the similarity between the DMU and the ideal solution from the similarity analysis of the information distance and the data sequence curve,so it makes the problem analysis more comprehensive.(6)To rank the efficiency of all DMUs,a DEA cross-efficiency analysis method based on competitive vision and interest correlation is proposed.This method explores the differences in competition intensity and related interests between DMUs,and the DMU can provide a corresponding degree of support and suppression to partners and competitors through different interest correlation coefficients and competition intensity.It better enhances the group advantage of DMU.