Selection of Cotton Fabrics Using Pythagorean Fuzzy TOPSIS Approach

The comfortability of fabric is essential for improving its wearability, and several properties affect the comfort. Assessment and selection of the best cotton fabrics among these properties involving fuzziness and uncertainty are regarded as a multicriteria decision-making (MCDM) problem. This research proposed the technique for order preference by similarity to ideal solution (TOPSIS) approach under Pythagorean fuzzy sets (PFSs) context to select the best cotton fabric among these properties. The PFSs are used to handle uncertain information on cotton fabrics, and the TOPSIS approach is applied to choose the best cotton fabric. Since the distance measure is a critical factor of TOPSIS, a sensitivity analysis with different parameter settings for the distance measure is performed to investigate the ranking effects. A comparative analysis is conducted between the Hamming and Euclidean distance measurements and sine trigonometric Pythagorean fuzzy weighted average and geometric aggregation operators. The comparative results have significantly high correlation coefficients with other distance measures and aggregation operators. The novelties of the proposed approach are that it not only can offer objective evaluations but also can be easily calculated and obtain reliable and reasonable results, and it can be applied to other textile areas.