International Journal of Fuzzy Systems

, Volume 21, Issue 8, pp 2650–2666 | Cite as

Two-Stage Fuzzy Cross-Efficiency Aggregation Model Using a Fuzzy Information Retrieval Method

  • Yan Huang
  • Ying-Ming WangEmail author
  • Jian Lin


In an uncertain environment, the process of fuzzy cross-evaluation in data envelopment analysis (DEA) models is explained by different types of fuzzy data. In this paper, the fuzzy cross-efficiency matrix with different types of fuzzy data will be changed into a cross-evaluation similarity matrix by similarity theory of generalized trapezoidal fuzzy numbers. Based on the fuzzy information retrieval (FIR) method, a new ordered-geometric-mean averaging operator is defined to yield aggregation weights by the relevance between the self-evaluation and cross-evaluation of each decision-making unit (DMU). Furthermore, the common neighbor DMU selection algorithm is constructed to classify the DMUs. Then, the fuzzy logic rules are proposed to modify the aggregation results by combining the clustering results. Finally, this paper demonstrates the applicability of fuzzy cross-efficiency aggregation using the FIR method by taking an example of 12 suppliers from the semiconductor industry.


Data envelopment analysis Cross-evaluation Aggregation Fuzzy information retrieval 



This research is supported by National Natural Science Foundation of China (No. 71801050, 71601049), Humanities and Social Science Foundation of the Ministry of Education (No. 16YJC630064), Natural Science Foundation of Fujian Province (No. 2019J01399), Fujian Social Science Planning Project (No. FJ2017C033).


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© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  1. 1.Decision Sciences InstituteFuzhou UniversityFuzhouPeople’s Republic of China
  2. 2.College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhouPeople’s Republic of China
  3. 3.Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of EducationFuzhou UniversityFuzhouPeople’s Republic of China

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