An Integrated Variance-COPRAS Approach with Nonlinear Fuzzy Data for Ranking Barriers Affecting Sustainable Operations

K. N. S. Venkata Ramana, Raghunathan Krishankumar, Maja Sudjicki Trzin, P. P. Amritha and Dragan Pamucar
Additional contact information
K. N. S. Venkata Ramana: Department of Computer Science and Engineering, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
Raghunathan Krishankumar: Department of Computer Science and Engineering, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
Maja Sudjicki Trzin: FEFA Faculty, Business Economics, 101801 Belgrade, Serbia
P. P. Amritha: TIFAC CORE in Cyber Security, Amrita School of Engineering Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore 64112, India
Dragan Pamucar: Department of Logistics, Military Academy, University of Defence Belgrade, 11000 Belgrade, Serbia

Sustainability, 2022, vol. 14, issue 3, 1-18

Abstract: Sustainability is becoming the core theme of every organization to protect the planet from the drastic effects of climate change. Many organizations have drastically changed their practices to encourage green habits for sustainable operations. Practitioners have discussed the difficulties in the literature owing to the adoption of sustainable aspects of environmental, economic and social paradigms in the organization. One can identify diverse barriers, and ranking them would help policy-makers plan their actions. Motivated by this claim, a new integrated approach with nonlinear fuzzy data is put forward in this paper. The nonlinear mapping of fuzzy data provides a better representation of uncertainty, which inspired the authors to use nonlinear data. Further, the attitudinal variance method is proposed for a weight assessment of the criteria that can handle hesitation effectively and consider each agent’s reliability. The Boran principle in the nonlinear context is used to calculate the reliability values. Complex proportional assessment (COPRAS), a popular ranking algorithm, is extended to nonlinear data for rationally ranking barriers that affect sustainable operations. An illustrative example exemplifies the usability of the approach, and a comparison/sensitivity analysis reveals the pros and cons of the framework.

Keywords: barrier ranking; complex proportional assessment; nonlinear fuzzy data; sustainable operations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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