Diagnosing social failures in sustainable supply chains using a modified Pythagorean fuzzy distance to ideal solution. (April 2021)

• Record Type: Journal Article
• Authors: Hendiani, Sepehr; Lev, Benjamin; Gharehbaghi, Afsaneh
• Is Part Of: Computers & industrial engineering. Volume 154(2021)
• Publication Date: 2021-04
• Subjects: Supply chain management -- Corporate social responsibility -- Social sustainability -- Interval-valued Pythagorean fuzzy sets -- Sustainable development; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107156
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A new fuzzy distance to ideal solution is proposed using IVPF mathematical principles. A sustainability evaluation approach has been proposed facilitating the complexities of previous IVPF MCDM approaches. A generic social sustainability framework is proposed consolidating the most common criteria. Identification of weak performing social criteria has been explored using the new distance mechanism. Abstract: Social sustainability can be mentioned as one of the pivotal objectives towards sustainable development which has received the least attention comparing to environmental and economic dimensions during these past years. Due to its impact on organization's competitive power, researchers have proposed models to measure social performance in supply chains. However, most of these researches reveal shortcomings once encountering the cases with a huge number of criteria due to their complex computations. In order to fill this gap, this study proposes a new soft computing multi-criteria interval-valued Pythagorean fuzzy distance to ideal solution approach based on interval-valued Pythagorean closeness which performs outstandingly in cases with a high fluctuation in the number of criteria. A new mechanism is defined to distinguish the weak performing social factors through supply chains by classifying them into four categorize based on their performance and distance to the best performing factors. This approach is unique in the sense that it both covers a remarkable amount of uncertainty and eases the computational processes of the previous multi-criteria decision making approaches by modifying the steps to select the most ideal solution. The feasibility and applicability of this approach have been validated by applications to a numerical case and comparative analysis.