A Robust Decision-Making Approach for Supplier Selection Using Complex Picture Fuzzy Information Involving Prioritization of Attributes

Abstract

To reduce the risk and interruptions of the supply chain, some appropriate aggregation operators are used to deal with crucial challenges and to keep the support system reliable. In this article, we explore the notion of the complex picture fuzzy (CPF) set (CPFS), a wider framework of fuzzy and picture fuzzy sets. Some robust operations of the Aczel Alsina aggregation tools are characterized based on CPF information to overcome the impact of uncertain and imprecision situations. We also explain an innovative concept of the prioritized aggregation operators (AOs) in the light of CPF information. The theory of unknown weight vectors is used to develop some new approaches of the CPF environments, including CPF Aczel Alsina prioritized average (CPFAPA) and CPF Aczel Alsina priortized geometric (CPFAPG) operators. We also propose a class of new methodologies under considering CPF information, such as CPF Aczel Alsina prioritized weighted average (CPFAPWA) and CPF Aczel Alsina prioritized weighted geometric (CPFAPWG) operators. To illustrate the flexibility of our invented methodologies, some appropriate characteristics of the proposed approaches are also demonstrated. The multi-attribute decision-making (MADM) technique is used to evaluate many crucial real-life challenges. An algorithm for the MADM problem is also proposed to show the robustness of our invented approaches. By using proposed methodologies, we demonstrate an experimental case study to choose a sustainable supplier agnet based on CPF information. To show the practicability and consistency of our invented approaches by contrasting the results of existing approaches in the literature review with currently presented methodologies.

Keywords

• Complex picture fuzzy number
• prioritized aggregation operators
• Aczel Alsina operations
• supplier selection
• a multi-attribute decision-making system