Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique

Priority degrees for hesitant fuzzy sets: Application to multiple attribute decision making

Research on multi-objective decision-making under cloud platform based on quality function deployment and uncertain linguistic variables

Hesitant fuzzy sets (HFSs) are widely applied in pattern recognition, classification, clustering, and multiple attribute decision making. In order to get more accurate decision results, the order relation of HFSs is particularly important. In this paper, some defects of the existing order relations for HFSs are discussed. In order to solve these problems, by employing a distance measure and the TOPSIS method, we propose a new order relation extraction method based on a new additive consistency fuzzy preference relation for hesitant fuzzy elements (HFEs). Then, the proposed additive consistency fuzzy preference relation is applied to integrate group decision information. In multi-attribute group decision making (MAGDM), it is particularly important to ensure the consensus of the decision-makers (DMs), and the consistency of the decision process is the precondition for DMs to reach consensus. The proposed method can maintain the consistency of the decision process for MAGDM under hesitant fuzzy environments, so as to get the consensus of DMs, besides, it can overcome the limitations of the existing order relations for HFSs. At the end of this paper, a numerical example is used to illustrate the effectiveness and feasibility of the new approach, and some comparative analyses are given. The obtained results confirm the theoretical and numerical analyses and emphasize the advantages, which can ensure the consistency of the whole decision process and avoid the original decision information change and loss of the proposed method, so as to be more in line with the actual situation.

Multi-attribute group decision making based on hesitant fuzzy sets, TOPSIS method and fuzzy preference relations

As a new intelligent manufacturing model, the Internet of Manufacturing Things (IOMT) based on the Internet of Things technology has a large amount of random, fuzzy, and uncertain information, which makes the optimization of manufacturing services face challenges. On the basis of fully considering the uncertain factors in the process of manufacturing service, a quality of service (QoS) optimization method based on mixed information axioms is proposed to solve the optimization problem of manufacturing service in the IOMT environment. The mixed uncertainty model is expounded with random variables to express the range of system and fuzzy variables to describe the design range. In order to select the best resource or sort, the fuzzy mathematics and the axiomatic theory of axiomatic design theory are combined to evaluate the coexistence of ambiguity and randomness of the QoS. Finally, the process of treatment and optimization in practical application by taking the example of a hydraulic pump product seeking service through the civil aircraft IOMT service platform is described. This example is given out to demonstrate the effectiveness of the proposed method by comparing with the traditional information axiom method, and this method extends the application scope of traditional information axioms.

Service Optimization of Internet of Manufacturing Things Based on Mixed Information Axioms

A hesitant fuzzy multiple attribute decision making method based on linear programming and TOPSIS

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A hesitant fuzzy multiple attribute decision making method based on linear programming and TOPSIS