A Novel Decision Model for Surgical Robot Selection Based on the Bipolar Fuzzy Z-number

Abstract

Abstract

Multi-criteria group decision-making (MCGDM) is essential in complex environments; however, existing approaches often fail to simultaneously address bipolar information and the reliability of expert judgments, leading to less robust decision outcomes. In healthcare applications such as surgical robot selection, this limitation becomes critical due to the involvement of uncertain and conflicting evaluations. To overcome this gap, this study proposes a novel Bipolar Fuzzy Z-number (BFZN)-based MCGDM framework that integrates bipolar fuzzy sets with Z-numbers to effectively model both uncertainty and reliability. New operational laws based on Dombi t-norms are developed, along with a Bipolar Fuzzy Z-number Dombi Weighted Average (BFZNDWA) aggregation operator. Furthermore, the classical Simple Additive Weighting (SAW) method is extended to the BFZN environment to improve ranking accuracy and discrimination among alternatives. A case study on surgical robot selection demonstrates that the proposed model provides more consistent, reliable, and informative decision results compared to existing methods. The proposed framework is computationally feasible and can be generalized to other decision-making problems involving complex uncertain and reliability-based information.