A multi-distance interval-valued neutrosophic approach for social failure detection in sustainable municipal waste management
Developing sustainable municipal waste management systems requires an in-depth analysis and synthesis of economic, environmental, and social sustainable development indicators. However, despite its profound impact on organizational performance, social sustainability has received little attention in previous studies compared to economic and environmental sustainability. Although a few studies have been conducted to analyze and measure the impact of social sustainability indicators, most of these endeavors fail to consider many indicators that must be evaluated under uncertain and incomplete information. This study proposes a new decision model that implements Interval-Valued Neutrosophic Sets (IVNS) within a multi-distance measure defined with respect to an ideal reference solution. IVNS allows decision-makers to reliably express their opinions using truth, indeterminacy, and falsity membership functions. A linguistic framework is developed to categorize indicators based on their performance and suggest potential solutions when detecting indicators with relatively weak performances. Social sustainability failures in the municipal waste management system of Istanbul are investigated to show the applicability and efficacy of the proposed approach. We identify salary satisfaction and health insurance as the most significant social indicators determining the success of the system, while freedom of association and citizen participation are categorized as the worst-performing ones. The stability of the proposed methodology is illustrated by performing a comparative analysis with Single-Valued Neutrosophic Sets and Interval-Valued Fuzzy Pythagorean Sets.
Torkayesh, Ali Ebadi; Tavana, Madjid; and Santos-Arteaga, Francisco J., "A multi-distance interval-valued neutrosophic approach for social failure detection in sustainable municipal waste management" (2022). Business Systems and Analytics Faculty Work. 29.