Multi-granularity unbalanced linguistic group decision-making with incomplete weight information based on VIKOR method

Decision makers (DMs) adopt multi-granularity linguistic assessments to form decision matrixes as a result of different backgrounds and experience, which is common in the process of group decision-making (GDM). Especially, unbalanced linguistic term sets (ULTS), as a representative linguistic representation, are employed in numerous fields. To deal with the problem of different granularities, the linguistic hierarchies (LH) are introduced to transform different level of granularities into the same level. In addition, incomplete attribute weights are considered, which are obtained by solving a multi-objective optimization model. Furthermore, extending Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to multi-granularity ULTS with incomplete attribute weight in GDM is proposed. Finally, an example concerning green supplier selection and a comparative analysis are carried out to illustrate the proposed method and find that group or personal opinions can be reflected according to actual situations compared with other approaches.

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