An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms

•Develop a novel Fuzzy Network DEA approach using adjustable possibilistic programming.•Reviews real-world applications of FNDEA literature.•The proposed fuzzy network DEA model is flexible, adjustable, general, and applicable.•The FNDEA method is capable to rank of Two-stage DMUs under fuzzy data.•...

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Vydané v:Expert systems with applications Ročník 166; s. 113938
Hlavní autori: Peykani, Pejman, Mohammadi, Emran, Emrouznejad, Ali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 15.03.2021
Elsevier BV
Predmet:
Adjustable possibilistic programming
Confidence intervals
Data analysis
Data envelopment analysis
Decision making
Efficiency
Fuzzy chance-constrained programming
Fuzzy logic
Fuzzy sets
Investment
Investment firms
Linearity
Network data envelopment analysis
Performance appraisal
Portfolio management
Ranking
Two-stage structure
Two-stage structure
Fuzzy chance-constrained programming
Investment firms
Adjustable possibilistic programming
Network data envelopment analysis
ISSN:0957-4174, 1873-6793
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Abstract •Develop a novel Fuzzy Network DEA approach using adjustable possibilistic programming.•Reviews real-world applications of FNDEA literature.•The proposed fuzzy network DEA model is flexible, adjustable, general, and applicable.•The FNDEA method is capable to rank of Two-stage DMUs under fuzzy data.•The presented approach is applied to the performance evaluation of investment firms. This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment analysis (FNDEA) approach. The main advantages of the proposed FNDEA approach can be summarized as follows: linearity of the proposed FNDEA models, unique efficiency decomposing under ambiguity, capability to extending for other network structures. Moreover, FNDEA approach can be applied for ranking of two-stage DMUs under fuzzy environment in three stages: 1) solving the proposed FNDEA model for all optimistic-pessimistic viewpoints and confidence levels, 2) then plotting the results and drawing the surface of all efficiency scores, 3) and finally calculate the volume of the three-dimensional shape in below the efficiency surface. This volume can be as ranking criterion. Remarkably, the presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process. Illustrative results of the real-life case study show that the proposed approach is effective and practically very useful.
AbstractList •Develop a novel Fuzzy Network DEA approach using adjustable possibilistic programming.•Reviews real-world applications of FNDEA literature.•The proposed fuzzy network DEA model is flexible, adjustable, general, and applicable.•The FNDEA method is capable to rank of Two-stage DMUs under fuzzy data.•The presented approach is applied to the performance evaluation of investment firms. This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment analysis (FNDEA) approach. The main advantages of the proposed FNDEA approach can be summarized as follows: linearity of the proposed FNDEA models, unique efficiency decomposing under ambiguity, capability to extending for other network structures. Moreover, FNDEA approach can be applied for ranking of two-stage DMUs under fuzzy environment in three stages: 1) solving the proposed FNDEA model for all optimistic-pessimistic viewpoints and confidence levels, 2) then plotting the results and drawing the surface of all efficiency scores, 3) and finally calculate the volume of the three-dimensional shape in below the efficiency surface. This volume can be as ranking criterion. Remarkably, the presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process. Illustrative results of the real-life case study show that the proposed approach is effective and practically very useful.
This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment analysis (FNDEA) approach. The main advantages of the proposed FNDEA approach can be summarized as follows: linearity of the proposed FNDEA models, unique efficiency decomposing under ambiguity, capability to extending for other network structures. Moreover, FNDEA approach can be applied for ranking of two-stage DMUs under fuzzy environment in three stages: 1) solving the proposed FNDEA model for all optimistic-pessimistic viewpoints and confidence levels, 2) then plotting the results and drawing the surface of all efficiency scores, 3) and finally calculate the volume of the three-dimensional shape in below the efficiency surface. This volume can be as ranking criterion. Remarkably, the presented fuzzy network DEA approach is implemented for performance appraisal and ranking of investment firms (IFs) with two-stage processes including operational and portfolio management process. Illustrative results of the real-life case study show that the proposed approach is effective and practically very useful.
ArticleNumber 113938
Author Emrouznejad, Ali
Mohammadi, Emran
Peykani, Pejman
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  surname: Peykani
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  organization: School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
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  orcidid: 0000-0001-9965-1118
  surname: Mohammadi
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  organization: School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
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  givenname: Ali
  orcidid: 0000-0001-8094-4244
  surname: Emrouznejad
  fullname: Emrouznejad, Ali
  email: a.emrouznejad@aston.ac.uk
  organization: Aston Business School, Aston University, Birmingham, UK
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Keywords Two-stage structure
Fuzzy chance-constrained programming
Investment firms
Adjustable possibilistic programming
Network data envelopment analysis
Language English
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Snippet •Develop a novel Fuzzy Network DEA approach using adjustable possibilistic programming.•Reviews real-world applications of FNDEA literature.•The proposed fuzzy...
This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of...
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StartPage 113938
SubjectTerms Adjustable possibilistic programming
Confidence intervals
Data analysis
Data envelopment analysis
Decision making
Efficiency
Fuzzy chance-constrained programming
Fuzzy logic
Fuzzy sets
Investment
Investment firms
Linearity
Network data envelopment analysis
Performance appraisal
Portfolio management
Ranking
Two-stage structure
Title An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms
URI https://dx.doi.org/10.1016/j.eswa.2020.113938
https://www.proquest.com/docview/2480005348
Volume 166
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