q-Rung orthopair fuzzy number (qROFN) is a flexible and superior fuzzy information description tool which can provide stronger expressiveness than intuitionistic fuzzy number and Pythagorean fuzzy number. Muirhead mean (MM) operator and its dual form geometric MM (GMM) operator are two all-in-one aggregation operators for capturing the interrelationships of the aggregated arguments because they are applicable in the cases in which all arguments are independent of each other, there are interrelationships between any two arguments, and there are interrelationships among any three or more arguments. Archimedean T-norm and T-conorm (ATT) are superior operations that can generate general and versatile operational rules to aggregate arguments. To take advantage of qROFN, MM operator, GMM operator, and ATT in multicriteria group decision making (MCGDM), an Archimedean MM operator, a weighted Archimedean MM operator, an Archimedean GMM operator, and a weighted Archimedean GMM operator for aggregating qROFNs are presented to solve the MCGDM problems based on qROFNs in this paper. The properties of these operators are explored and their specific cases are discussed. On the basis of the presented operators, a method for solving the MCGDM problems based on qROFNs is proposed. The effectiveness of the proposed method is demonstrated via a numerical example, a set of experiments, and qualitative and quantitative comparisons. The demonstration results suggest that the proposed method has satisfying generality and flexibility at aggregating q-rung orthopair fuzzy information and capturing the interrelationships of criteria and the attitudes of decision makers and is feasible and effective for solving the MCGDM problems based on qROFNs.
In this paper, a novel multiple attribute decision making method based on a set of Archimedean power Maclaurin symmetric mean operators of picture fuzzy numbers is proposed. The Maclaurin symmetric mean operator, power average operator, and operational rules based on Archimedean T-norm and T-conorm are introduced into picture fuzzy environment to construct the aggregation operators. The formal definitions of the aggregation operators are presented. Their general and specific expressions are established. The properties and special cases of the aggregation operators are respectively explored and discussed. Using the presented aggregation operators, a method for solving the multiple attribute decision making problems based on picture fuzzy numbers is designed. The method is illustrated through example and experiments and validated by comparisons. The results of the comparisons show that the proposed method is feasible and effective that can provide the generality and flexibility in aggregation of values of attributes and consideration of interactions among attributes and the capability to lower the negative effect of biased attribute values on the result of aggregation.
Two important tasks in multiattribute group decision-making (MAGDM) are to describe the attribute values and to generate a ranking of all alternatives. A superior tool for the first task is linguistic interval-valued intuitionistic fuzzy number (LIVIFN), and an effective tool for the second task is aggregation operator (AO). To date, nearly ten AOs of LIVIFNs have been presented. Each AO has its own features and can work well in its specific context. But there is not yet an AO of LIVIFNs that can offer desirable generality and flexibility in aggregating attribute values and capturing attribute interrelationships and concurrently reduce the influence of unreasonable attribute values. To this end, a linguistic interval-valued intuitionistic fuzzy Archimedean power Muirhead mean operator and its weighted form, which have such capabilities, are presented in this paper. Firstly, the generalised expressions of the AOs are established by a combination of the Muirhead mean operator and the power average operator under the Archimedean T-norm and T-conorm operations of LIVIFNs. Then the properties of the AOs are explored and proved, their specific expressions are constructed, and the special cases of the specific expressions are discussed. After that, a new method for solving the MAGDM problems based on LIVIFNs is designed on the basis of the weighted AO. Finally, the designed method is illustrated via a practical example, and the presented AOs are evaluated via experiments and comparisons.
Purpose: The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints. Design/methodology/approach: This approach uses a case-based reasoning framework and firstly formalizes existing online complaints and their solutions, new online complaints, and complaint products, problems, and content as source cases, target cases, and distinctive features of each case, respectively. Then the process of using existing word-level, sense-level, and text-level measures to assess the similarities between complaint products, problems, and contents is explained. Based on these similarities, a measure with high accuracy for assessing the overall similarity between cases is designed. The effectiveness of the approach is evaluated by numerical and empirical experiments. Findings:The evaluation results show that a measure simultaneously considering the features of similarity at word, sense, and text levels can obtain higher accuracy than those measures considering only one level feature of similarity and the designed measure is more accurate than all of its linear combinations. Practical implications:The approach offers a feasible way to reduce manual intervention in online complaint handling. Complaint products, problems and content should be synthetically considered when handling an online complaint. The designed procedure of the measure with high accuracy can be applied in other applications that consider multiple similarity features or linguistic levels.Originality/value: A method for linearly combining the similarities at all linguistic levels to accurately assess the overall similarities between online complaint cases is presented. This method is experimentally verified to be helpful to improve the accuracy of online complaint case retrieval. This is the first consideration of the accuracy of the similarity measures for online complaint case retrieval. () () ), ,) J J J ) c v ) J J J
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