文章基本信息

标题：HFLTS-DEA Model for Benchmarking Qualitative Data
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作者：Hartono ; Erianto Ongko ; Dahlan Abdullah 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2019
卷号：11
期号：2
页码：109-131
出版社：International Center for Scientific Research and Studies
摘要：In Data Envelopment Analysis (DEA), performance evaluation is generally assumed to be based on a set of quantitative data. In many real-world environments, however, it is necessary to take into account the presence of qualitative factors in assessing the performance of decision-making units. The easiest thing to do is to give an assessment to the input and output values of each Decision-Making Unit (DMU) in the form of scale eg 1 is the best and 5 is the worst. The benchmarking process in qualitative data often creates problems when at the same time some people are giving judgments. Some people who provide an assessment may be able to provide different assessments, perhaps also hesitancy when assessing. The advantage of the Hesitant Fuzzy Linguistic Term Sets (HFLTS) model is that it can provide value for each Input and Output of DMU based on a qualitative and sometimes hesitancy-based assessment. The value provided by HFLTS will be used for the benchmarking process with Data Envelopment Analysis (DEA). Some qualitative data measurements involving Likert scale and Ordinal Data approaches have disadvantages when there are some assessors that provide judgment and can not model the computational trust considering hesitancy, vagueness, and uncertainty. The results of this study indicate that in HFLTS-DEA, the assessor can perform a good assessment in the form of qualitative data on the input and output of each DMU and then there will be available HFLTS evaluation results for use in the benchmarking process with DEA.
关键词：Computational Trust; Benchmarking; Data Envelopment Analysis; Hesitant Fuzzy Linguistic Term Sets; Qualitative Data