Aggregation of dual hesitant fuzzy heterogenous related information with extended Bonferroni mean and its application to MULTIMOORA. (September 2019)

• Record Type: Journal Article
• Authors: Liang, Decui; Darko, Adjei Peter; Xu, Zeshui; Wang, Mingwei
• Is Part Of: Computers & industrial engineering. Volume 135(2019)
• Page Start: 156
• Page End: 176
• Publication Date: 2019-09
• Subjects: Dual hesitant fuzzy sets -- MULITIMOORA -- Extended Bonferroni mean -- Bonferroni mean -- Rough sets; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.05.029
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Considering the heterogenous interrelationship of attributes, we design extended BM. We modify the MULTIMOORA method for the dual hesitant fuzzy MADM. This paper propose a DHFEBM-based MULTIMOORA method. We determine the weights of attributes via the attribute significance of rough set. Abstract: As an effective evaluation format, dual hesitant fuzzy sets (DHFSs) have became a powerful technique in addressing uncertainty and fuzziness in decision making. Considering the heterogenous interrelationship among attributes, we deeply investigate the extended Bonferroni mean (EBM) and the extended geometric Bonferroni mean (EGBM) under the dual hesitant environment. In this case, some attributes may be related to only a non-empty subset of the rest of the attributes, and others have no relationship with the remaining attributes. Concretely speaking, we develop the dual hesitant fuzzy extended Bonferroni mean (DHFEBM), the weighted dual hesitant fuzzy extended Bonferroni mean (WDHFEBM), the dual hesitant fuzzy extended geometric Bonferroni mean (DHFEGBM) and the weighted dual hesitant fuzzy extended geometric Bonferroni mean (WDHFEGBM) aggregation operators, respectively. At the same time, some of their special cases and properties are investigated. Then, based on the WDHFEBM and the WDHFEGBM operators, we further designs a DHFEBM-based MULTIMOORA method. Before the analysis, we determine the weight information of the attributes with the aid of the attribute significance of rough set theory in advance. Subsequently, the robustness of the ranking MULTIMOORA technique is enhanced by the Bonferroni mean (BM). Finally, an illustrative example of renewable energy technology selection demonstrates the applicability of the proposed method.