An Adjustable Fuzzy Chance-Constrained Network DEA Approach with Application to Ranking 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.

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Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Adjustable possibilistic programming,Fuzzy chance-constrained programming,Investment firms,Network data envelopment analysis,Two-stage structure,Engineering(all),Computer Science Applications,Artificial Intelligence
Publication ISSN: 1873-6793
Last Modified: 28 May 2024 07:17
Date Deposited: 14 Sep 2020 11:34
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Related URLs: https://www.sci ... 957417420307284 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-03-15
Published Online Date: 2020-09-12
Accepted Date: 2020-08-27
Authors: Peykani, Pejman
Mohammadi, Emran
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)

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