New q-rung orthopair fuzzy partitioned Bonferroni mean operators and their application in multiple attribute decision making

Abstract

The q-rung orthopair fuzzy sets are superior to intuitionistic fuzzy sets or Pythagorean fuzzy sets in expressing fuzzy and uncertain information. In this paper, some partitioned Bonferroni means (BMs) for q-rung orthopair fuzzy values have been developed. First, the q-rung orthopair fuzzy partitioned BM (q-ROFPBM) operator and the q-rung orthopair fuzzy partitioned geometric BM (q-ROFPGBM) operator are developed. Some desirable properties and some special cases of the new aggregation operators have been studied. The q-rung orthopair fuzzy weighted partitioned BM (q-ROFWPBM) operator and the q-rung orthopair fuzzy partitioned geometric weighted BM (q-ROFPGWBM) operator are also developed. Then, a new multiple-attribute decision-making method based on the q-ROFWPBM (q-ROFPGWBM) operator is proposed. Finally, a numerical example of investment company selection problem is given to illustrate feasibility and practical advantages of the new method. K E Y W O R D S aggregation operator, Bonferroni mean, multiple attribute decision making, q-rung orthopair fuzzy sets Int J Intell Syst. 2019;34:439-476.wileyonlinelibrary.com/journal/int

Generalized orthopair fuzzy weighted distance‐based approximation (WDBA) algorithm in emergency decision‐making

“…Combining the diverse Maclaurin symmetric mean (MSM) operators with q‐ ROFS, the q‐ rung orthopair fuzzy partitioned MSM operators, the q‐ rung orthopair fuzzy power MSM operators, and the q‐ rung orthopair fuzzy MSM operators are presented. Combining the diverse Bonferroni mean (BM) operators with q‐ ROFS, the q‐ Rung orthopai fuzzy BM operators, the q‐ rung orthopair fuzzy partitioned BM operators, and the q‐ rung orthopair fuzzy extended BM operators are derived. Xing et al developed some q‐ rung orthopair fuzzy point weighted aggregation operators for integrating the decision preference information. …”

“…">Decision‐making technologies. In addition to the aggregation operators mentioned above can be used in aggregating the overall preference information. Wang and Li proposed a q‐ rung orthopair fuzzy TODIM method based on the prospect theory for obtaining the ranking result of green suppliers.…”

“… The existing aggregation operators employing in MADM have counterintuitive phenomena and low resolution in distinguishing the optimal alternative in real life. It may be untoward or improper for DMs to choose optimal or receivable alternatives.…”

“…Hence, the second motivation of this paper is to introduce a new score function, which can take the hesitation information into consideration. The existing q‐ rung orthopair fuzzy weighting determine model only consider objective weight or subjective weight …”

Some q ‐rung orthopair fuzzy Hamy mean operators in multiple attribute decision‐making and their application to enterprise resource planning systems selection

“…Wei et al developed the q‐ rung orthopair fuzzy generalized Heronian mean ( q‐ ROFGHM) operator, q‐ rung orthopair fuzzy geometric Heronian mean ( q‐ ROFGHM) operator, q‐ rung orthopair fuzzy generalized weighted Heronian mean ( q‐ ROFGWHM) operator, and q‐ rung orthopair fuzzy weighted geometric Heronian mean ( q‐ ROFWGHM) operator. Yang and Pang defined some new q‐ rung orthopair fuzzy partitioned Bonferroni mean operators. Wang et al defined some similarity measures of q‐ ROFSs based on cosine function.…”