Research Information System
Applied Soft Computing, vol.199, 2026 (SCI-Expanded, Scopus)
Post-consumer textile waste (PCTW) refers to textile products that owners decide not to use and mainly carelessly abandon. The growing volume of these waste flows, connected with insufficient recycling infrastructure, has made PCTW a critical issue for environmental sustainability, as it significantly contributes to resource depletion, energy consumption, and pollution. This research aims to provide a decision model for categorizing the PCTW flow treatment solutions. The formulated multi-criteria framework improves traditional models, such as ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI) and comparisons between ranked criteria (COBRAC). The presented trigonometric COBRAC nonlinear model enables efficient processing of uncertainty in expert preferences and allows consistent definition of the relationships between criteria, regardless of the structure and dimensions of the problem. The integration of fuzzy Frank soft functions into the RAFSI methodology further enables the simulation of different scenarios and risk assessment in a dynamic environment. Additionally, fuzzy Frank's soft function provides flexible nonlinear processing of information and allows observation of mutual relationships between criteria. The application of a nonlinear function for aggregation contributes to a more objective evaluation, especially in situations where the relationships between variables are complex or unequal, ultimately enabling rational decision-making and a better understanding of the problem being solved. The feasibility of the introduced fuzzy trigonometric COBRAC-Frank soft RAFSI model is supported by the real-world case study application in the context of managing PCTW flows. It is concluded that the alternatives “recycling” and “material recovery” represent dominant and stable management strategies. The robustness of fuzzy trigonometric COBRAC-Frank soft RAFSI and the findings are supported by extensive sensitivity analyses. Managerial implications are elaborated on to improve the textile industry's sustainability.