A decision-making mechanism for multi-attribute group decision-making using 2-tuple linguistic T-spherical fuzzy maximizing deviation method

Hospital performance evaluation is vital for effective hospital management as it provides valuable information about a hospital’s condition and enables adaptable implementation based on various attributes. In this research, a multi-attribute group decision-making (MAGDM) method using a 2-tuple linguistic T-spherical fuzzy set (2TLT-SFS) is proposed in the context of the cognitive information presented in the hospital evaluation process. The T-spherical fuzzy set is the most advanced generalization of the q-rung orthopair fuzzy set (q-ROFS) which is capable of handling the uncertainty, fuzziness and ambiguity in terms of four parameters: positivity (yes), negativity (no), impartiality (abstain), and denial (non-acceptance). The 2-tuple linguistic terminology is used to measure the validity of ambiguous data. We propose the 2TLT-SF Hamy mean (2TLT-SFHM) operator, 2TLT-SF weighted Hamy mean (2TLT-SFWHM) operator, 2TLT-SF dual Hamy mean (2TLT-SFDHM) operator and 2TLT-SF weighted dual Hamy mean (2TLT-SFWDHM) operator by combining the 2TLT-SFS and HM operator. Then, based on the proposed maximizing deviation method, a new optimization model is built that is able to exploit expert preference to find the best objective weights among attributes. Next, we extend the TOPSIS (technique for establishing order preference by similarity to the ideal solution) method to the 2TLT-SF-TOPSIS version which not only accounts for human cognition’s inherent uncertainty but also allows experts a wider context to express their decision. Finally, we give a case study about the selection of key performance indicators for hospital performance evaluation to support our proposed method. The findings from parameter analysis and comparative analysis demonstrate the method’s efficacy and reliability. The outcomes demonstrate that our approach successfully handles the assessment and choice of key performance indicators for hospital performance evaluation and captures the relationship between any number of attributes.