Optimizing medical service robot selection with spherical fuzzy rough MAUT approach

Multicriteria decision-making (MCDM) models are valuable for handling complex problems with various criteria and choices. Conventional MCDM lacks a generic paradigm for many real-world situations, like group choices and ambiguous preferences, and only considers crisp decision problems. Therefore, it makes sense to consider most MCDM problems in the actual world to be fuzzy MCDM problems. Spherical fuzzy sets extend the concept of fuzzy sets by considering uncertainty in three ways rather than fuzzy sets' membership degrees only. Effectively identifying, characterizing, and comparing the many variables that may affect a decision is the multi-attribute utility theory (MAUT) approach. This research proposes a novel and expanded MAUT approach that deals with rough approximations under a spherical fuzzy environment. The entropy weighting technique, a key component of our proposed method, assigns weights to criteria based on their relative importance, thereby providing a robust and objective basis for decision-making. This method is applied when selecting medication service robots (MSR) for COVID-19 patients. MSRs are increasingly being used in healthcare to reduce global fatality and spread rates, particularly for highly infectious diseases like COVID-19. However, selecting the most suitable robot from various options can be complex, necessitating mathematical tools for proper selection. The proposed method's effectiveness is not just a claim but is backed by sensitivity and comparative analyses, where we compared the results of our method with those of other established methods, demonstrating its superiority in terms of accuracy and efficiency.