Maclaurin Symmetric Mean Aggregation Operators and Their Application to Hesitant Q-Rung Orthopair Fuzzy Multiple Attribute Decision Making

The Maclaurin symmetric mean(MSM)operator exhibits a desirable characteristic by effectively capturing the correlations among multiple input parameters,and it serves as an extension of certain existing aggregation operators through adjustments to the parameter k.The hesitant q-rung orthopair set(Hq-ROFSs)can serve as an extension of the existing orthopair fuzzy sets,which provides decision makers more freedom in describing their true opinions.The objective of this paper is to present an MSM operator to aggregate hesitant q-rung orthopair numbers and solve the multiple attribute decision making(MADM)problems in which the attribute values take the form of hesitant q-rung orthopair fuzzy sets(H-qROFSs).Firstly,the definition of H-qROFSs and some operational laws of H-qROFSs are proposed.Then we develop a family of hesitant q-rung orthopair fuzzy maclaurin symmetric mean aggregation operators,such as the hesitant q-rung orthopair fuzzy maclaurin symmetric mean(Hq-ROFMSM)operator,the hesitant q-rung orthopair fuzzy weighted maclaurin symmetric mean(Hq-ROFWMSM)operator,the hesitant q-rung orthopair fuzzy dual maclaurin symmetric mean(Hq-ROFDMSM)operator,the hesitant q-rung orthopair fuzzy weighted dual maclaurin symmetric mean(Hq-ROFWDMSM)operator.And the properties and special cases of these proposed operators are studied.Furthermore,an approach based on the Hq-ROFWMSM operator is proposed for multiple attribute decision making problems under hesitant q-rung orthopair fuzzy environment.Finally,a numerical example and comparative analysis is given to illustrate the application of the proposed approach.

Evaluating and visualizing QoS of service providers in knowledgeintensive crowdsourcing:a combined MCDM approach

Purpose-As the number of joined service providers(SPs)in knowledge-intensive crowdsourcing(KI-C)continues to rise,there is an information overload problem for KI-C platforms and consumers to identify qualified SPs to complete tasks.To this end,this paper aims to propose a quality of service(QoS)evaluation framework for SPs in KI-C to effectively and comprehensively characterize the QoS of SPs,which can aid the efficient selection of qualified SPs.Design/methodology/approach-By literature summary and discussion with the expert team,a QoS evaluation indicator system for SPs in KI-C based on the SERVQUAL model is constructed.In addition,the Decision Making Trial and Evaluation Laboratory(DEMATEL)method is used to obtain evaluation indicators’weights.The SPs are evaluated and graded by the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)and rank-sum ratio(RSR),respectively.Findings-A QoS evaluation indicator system for SPs in KI-C incorporating 13 indicators based on SERVQUAL has been constructed,and a hybrid methodology combining DEMATEL,TOPSIS and RSR is applied to quantify and visualize the QoS of SPs.Originality/value-The QoS evaluation framework for SPs in KI-C proposed in this paper can quantify and visualize the QoS of SPs,which can help the crowdsourcing platform to realize differentiated management for SPs and assist SPs to improve their shortcomings in a targeted manner.And this is the first paper to evaluate SPs in KI-C from the prospect of QoS.