Aczel-Alsina power bonferroni aggregation operators for picture fuzzy information and decision analysis

Abstract

AbstractPicture fuzzy set (PFS) is an expedient mathematical approach for interpreting imprecise and nebulous information, and the power Bonferroni mean (PBM) operator is a crucial generalization of the power average (PA) operator, and the Bonferroni mean (BM) operator. Based on the Aczel-Alsina (AA), operational principles of PFS, we expand the PBM operator to integrate PFVs and develop a few AOs, namely PF Aczel-Alsina PBM (PFAAPBM) operator, weighted PF Aczel-Alsina PBM (WPFAAPBM) operator, PF Aczel-Alsina PGBM (PFAAPGBM) operator, and weighted geometric PF Aczel-Alsina PBM (WGPFAAPBM) operators respectively. These newly suggested PF Aczel-Alsina PBM operators can detect the connections between the membership, abstinence, and non-membership functions, which also maintain the important characteristics of the PBM operator. After that, we analyze a few enticing characteristics along with the particular applications of the suggested operators. Based on our suggested technique, we built an illustrated numerical example for the selection of competent research scientists to cope with MADM issues under the framework of PFVs. Finally, we contrast a few of our suggested methodologies with other prevailing methods to determine the feasibility and legitimacy of our suggested strategies.