A Linguistic Distribution-Based Approach to MALGDM with Multi-granular Unbalanced Hesitant Fuzzy Linguistic Information

LGDM with linguistic information has attracted attention from scholars because in linguistic LGDM problems, experts usually provide their assessments using linguistic expressions instead of exact numerical numbers due to the fuzziness and impreciseness of their knowledge [12] that is close to real world situations. In the literature, different models and approaches have been developed to deal with linguistic LGDM problems [8, 9, 16, 24]. Although previous models and approaches are effective for linguistic LGDM problems, there are still some challenges that need to be tackled.