Application of Fuzzy-Rough Approach in Tractor Selection

Abstract

The process of selecting a tractor exemplifies decision-making in a multi-criteria context. With the integration of the fuzzy-rough concept, this article employs a hybrid multi-criteria decision-making methodology. The fuzzy-rough approach is employed to facilitate decision-making with imprecise information, incorporating uncertainty while mitigating the subjectivity inherent in expert judgments. The Logarithm Methodology of Additive Weights (LMAW) method is utilized to evaluate the importance of criteria influencing the evaluation of selected tractors. Subsequently, the Simple Additive Weighting (SAW) method is employed to identify the optimal tractor aligning with the specified criteria. Among the five observed tractors, the Solis S 26 exhibited the most favorable results. Sensitivity analysis and result validation support the validity of the results obtained. Result validation involves a comparison of fuzzy-rough SAW outcomes with alternative methods utilizing the fuzzy-rough approach, while the influence of criteria importance on the decision's final outcome was examined through sensitivity analysis. This paper contributes to comprehending the fuzzy-rough concept's applicability in multi-criteria decision-making. Demonstrated flexibility of the fuzzy-rough methodology suggests its potential for future research reliant on imprecise data, uncertainty incorporation, and subjectivity reduction in decision-making processes.

Received: 19 December 2023 | Revised: 19 January 2024 | Accepted: 9 February 2024

Conflicts of Interest

Darko Božanić is an editorial board member for Journal of Computational and Cognitive Engineering and was not involved in the editorial review or the decision to publish this paper. The authors declare that they have no conflicts of interest to this work.