A Multi-Attribute Decision-Making Approach for the Analysis of Vendor Management Using Novel Complex Picture Fuzzy Hamy Mean Operators

Abstract

Vendor management systems (VMSs) are web-based software packages that can be used to manage businesses. The performance of the VMSs can be assessed using multi-attribute decision-making (MADM) techniques under uncertain situations. This article aims to analyze and assess the performance of VMSs using MADM techniques, especially when the uncertainty is of complex nature. To achieve the goals, we aim to explore Hany mean (HM) operators in the environment of complex picture fuzzy (CPF) sets (CPFSs). We introduce CPF Hamy mean (CPFHM) and CPF weighted HM (CPFWHM) operators. Moreover, the reliability of the newly proposed HM operators is examined by taking into account the properties of idempotency, monotonicity, and boundedness. A case study of VMS is briefly discussed, and a comprehensive numerical example is carried out to assess VMSs using the MADM technique based on CPFHM operators. The sensitivity analysis and comprehensive comparative analysis of the proposed work are discussed to point out the significance of the newly established results.

Decision algorithm for picture fuzzy sets and Aczel Alsina aggregation operators based on unknown degree of wights

“…We applied different existing AOs and obtained results after the assessment of the practical case study. AOs of PF information based on some appropriate degrees of weights Wei [ 58 ], Jana et al [ 63 ] produced some AOs of PFSs based on Dombi aggregation tools, Seikh and Mandal [ 64 ], some approaches of Frank aggregation tools under PF information are presented by the Khan et al [ 65 ], AOs of Hamacher aggregation tools based on PF information was developed by the Wei [ 55 ] and AOs of complex PFSs proposed by the Hussain et al [ 56 ]. All computed outcomes are listed in Table 10 .…”

“…Naeem et al [ 54 ] presented some major characteristics of Aczel Alsina aggregation tools to illustrate a list of new approaches based on PFSs. Moreover, adopting fuzzy logic in evaluating complex problems has been conducted in several research areas [ [55] , [56] , [57] ].…”

Evaluation of Electric Motor Cars Based Frank Power Aggregation Operators Under Picture Fuzzy Information and a Multi-Attribute Group Decision-Making Process

“…Seikh and Mandal [57] proposed some AOs based on Frank aggregation tools in the form of PF Frank weighted average (PFFWA) and PF Frank weighted geometric (PFFWG), AOs of PF Einstein weighted average (PFEWA) and PF Einstein weighted geometric (PFEWG) given by the Khan et al [60]. Wei [35] introduced some AOs of PF Hamacher weighted average (PFHWA) and PF Hamacher weighted geometric (PFHWG) operators, Senapati [61] proposed PF Aczel Alsina weighted average (PFAAWA), Naeem et al [62]also illustrate some new approaches of PF Aczel Alsina weighted geometric (PFAAWG) and Hussain et al [20]gave AOs of Complex PFSs under Hamy mean aggregation models. After applying all the above-discussed existing approaches under the consideration of the proposed algorithm, we stated all obtained results in Table 10.…”

“…Verma and Rohtagi [19]explored the concepts of similarity measures under a system of PF information and examined an application of medical diagnosis based on the decisionmaking process. Hussain et al [20]also presented some wellknown aggregation approaches to illustrate the technique of vendor management systems. Li et al [21]also showed the robustness of proposed mathematical aggregation approaches under consideration of a MADM problem.…”

Interval-Valued Pythagorean Fuzzy Information Aggregation Based on Aczel-Alsina Operations and Their Application in Multiple Attribute Decision Making

“…The Seikh and Mandal [38] elaborated an attractive methodologies of IFSs under the system of Dombi operations. Hussain et al [39] originated some robust aggregation models and also studied an application of vendor management system. Khan et al [40], [41] anticipated certain methodologies of bipolar valued hesitant fuzzy system and gave an attractive algorithm to handle loss of information during aggregating process.…”