Fermatean fuzzy TOPSIS-based approach for occupational risk assessment in manufacturing

Abstract

The importance of risk assessment in the context of occupational health and safety by manufacturing operators strengthens their hands in solving the problems they may encounter in business processes related to health and safety. One of the most important phases of conducting an exhaustive occupational risk assessment is to analyze potential hazards and associated risks quantitatively. Since manufacturing is one of the industries that require workers to be highly exposed to work, creating a safer environment to reduce occupational injuries is an important task. This study proposes a novel fuzzy risk assessment approach developed by integrating Fermatean fuzzy sets (FFSs) and technique for order preference by similarity to ideal solution (TOPSIS) method for ranking potential hazards in manufacturing. FFSs are a new version of fuzzy set theory that covers the intuitionistic fuzzy sets and Pythagorean fuzzy sets. This version of the fuzzy set is crucial in the decision-making process to handle uncertain information more easily and reflect uncertainty better. A linguistic scale under Fermatean fuzzy documentation has also been developed for experts/decision makers to disclose their judgments easily. Occupational risk analysts can benefit from this approach since FFSs are used for the first time in occupational risk assessment, and the approach is presented in integration with TOPSIS. The proposed approach is applied in the aluminum plate-manufacturing process risk assessment. In the conclusion of the implementation, risks arising in the production are prioritized. In addition, this study made comparisons with other fuzzy methods to demonstrate the proposed approach’s difference and practicality. This study’s results can support practitioners and risk analysts in formulating the improvement measures to increase the safety of the work environment further.

Multicriteria decision-making based on distance measures and knowledge measures of Fermatean fuzzy sets

“…Aydin ( 2021 ) proposed some cosine FF similarity measures with their use in decision-making. The applicability of the FFSs for occupational risk assesment in manufacturing was shown by Gul et al ( 2021 ). Some FF Hamacher arithematic and geometric aggregation operators and their application in MCDM was established by Hadi et al ( 2021 ).…”

Improved CoCoSo Method Based on Frank Softmax Aggregation Operators for T-Spherical Fuzzy Multiple Attribute Group Decision-Making

“…In contrast, the T-SFS is the most novel generalized fuzzy set, and it has greater expression space and freedom. In terms of methods for determining the optimal alternative, in addition to various aggregation operators, many alternative ranking techniques are prevailing in literature, such as TOPSIS (technique for order of preference by similarity to ideal solution) [ 8 ], VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) [ 9 ], MULTIMOORA (Multi-objective optimization based on the ratio analysis with the full multiplicative form) [ 10 ], TODIM (Portuguese acronym meaning Interactive Multi-Criteria Decision Making) [ 11 ], and CoCoSo [ 12 ]. Although these methods can solve the alternatives ranking problem, the most suitable one must depend on the structure of the decision-making problem.…”

A Rough-Fermatean DEMATEL Approach for Sustainable Development Evaluation for the Manufacturing Industry

“…FFs is a relatively novel variant of intuitionistic fuzzy theory, which was proposed by Senapati and Yager [ 19 ]. The FFs enhance the ability of Pythagorean fuzzy sets (PFs) to measure uncertainty, and to cover a wider range of information uncertainty, avoiding the loss of potential information in semantic transformations [ 21 ]. Many studies have applied the FFs-based approach to discuss MCDM issues in the real world, illustrating the effectiveness and reliability of FFs in reflecting expert’s uncertainty.…”

“…Many studies have applied the FFs-based approach to discuss MCDM issues in the real world, illustrating the effectiveness and reliability of FFs in reflecting expert’s uncertainty. [ 19 , 21 ]. On the other hand, most previous studies have used the method of arithmetic averaging to integrate the evaluation data from multiple experts; however, this approach is also prone to potential information loss.…”