Weighted Bonferroni Aggregation Operators on Complex q-Rung Orthopair 2-Tuple Linguistic Variables With Application to Green Supply Chain Management

Abstract

In this paper, we give advanced study in complex q-rung orthopair 2-tuple linguistic variables (CQRO2-TLVs). The major theme of the paper is to evaluate the novel concept of CQRO2-TLVs and their dominant operational laws so that it can be a competent procedure to assess ambiguous and erratic information in realistic decision problems. Furthermore, we derive the weighted Bonferroni aggregation operators with weighted Bonferroni mean (WBM) and weighted geometric Bonferroni mean (WGBM) based on the CQRO2-TLV information for exploring the complex q-rung orthopair 2-tuple linguistic WBM (CQRO2-TLWBM) and complex q-rung orthopair 2-tuple linguistic WGBM (CQRO2-TLWGBM) operators. Some flexible and reliable properties and theories for the CQRO2-TLWBM and CQRO2-TLWGBM operators are investigated. We then introduce two new techniques to manage the multi-attribute decision making (MADM) issues under the fuzzy environment based on these operators. We know that a green supply chain management integrates environmental, ethical, and social concerns that make more about environmental and social responsibility correlated with design, production and distribution. In this paper, we apply the proposed techniques to green supply chain management to express the efficacy and usefulness of the proposed techniques. We finally make the comparisons of the proposed operators with some existing operators that demonstrate the effectiveness of our proposed method.INDEX TERMS Fuzzy sets, Complex q-rung orthopair (CQRO) fuzzy sets, 2-tuple linguistic variables (2-TLVs), CQRO 2-TLVs, Weighted Bonferroni aggregation operators, Weighted Bonferroni mean (WBM), Green supply chain.