Probabilistic Hesitant Fuzzy Decision-Theoretic Rough Set Model and Its Application in Supervision of Shared Parking

A sophisticated three-way decision model utilizing a multi-granularity probabilistic hesitant fuzzy rough set is introduced to mitigate the issue of information loss arising from sets when characterizing information. Initially, properties are examined, subsequently proposing distance measure and function tailored these sets. Following this, multi-attribute group decision-making incorporating established, pertinent rules that satisfy minimal risk criteria presented. Building on this foundation, series critical steps for resolving category problems involve proposed. Lastly, with applied supervision urban shared parking platforms. The results indicate process based more reliable, outcome aligns actual situation, thus providing valuable references managers.