A new integrated multi-attribute decision-making approach for mobile medical app evaluation under q-rung orthopair fuzzy environment

Mobile medical app evaluation can be modelled as a multi-attribute decision-making (MADM) problem with multiple assessment attributes. Due to the increasing complexity and high uncertainty of decision environments, numerical numbers and/or traditional fuzzy sets may not be appropriate to model attribute information of mobile medical apps. In addition, heterogeneous relationships are often observed among different attributes in various practical decision situations. To deal with these issues, a q-rung orthopair fuzzy (q-ROF) MADM approach, which is a very powerful tool for describing vague information occurring in real decision circumstances, is proposed to handle decision-making problems in medical app evaluation. In particular, q-rung orthopair fuzzy numbers (q-ROFNs) are first applied to better express the preference information and expert assessment information. Then, q-ROFNs are extended by combining with Zhenyuan integral, resulting in the qROF Zhenyuan integral (q-ROFZI). This integral can capture complementary, redundant and/or independent characteristics among the attributes and is superior to existing operators on q-ROFNs. Next, based on the bestworst method (BWM) and Shapley value, two optimization models are constructed to objectively identify optimal fuzzy measures on the attribute set. Finally, a novel integrated q-ROF MADM approach is proposed and its computation procedure is presented and illustrated with its application to the problem of mobile medical app evaluation. A comparative analysis is carried out to demonstrate the validity, rationality, robustness and superiority of the developed method.