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Evaluation of medical service quality based on a novel multi-criteria decision-making method with unknown weighted information

100%

Zhao B. , Zhang R. , Xing Y.

Archives of Control Sciences

2021

tom Vol. 31, No. 3

645--685

In modern society, people concern more about the evaluation of medical service quality. Evaluation of medical service quality is helpful for medical service providers to supervise and improve their service quality. Also, it will help the public to understand the situation of different medical providers. As a multi-criteria decision-making (MCDM) problem, evaluation of medical service quality can be effectively solved by aggregation operators in interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) environment. Thus, this paper proposes interval-valued q-rung dual hesitant Maclaurin symmetric mean (IVq-RDHFMSM) operator and interval-valued q-rung dual hesitant weighted Maclaurin symmetric mean (IVq-RDHFWMSM) operator. Based on the proposed IVq-RDHFWMSM operator, this paper builds a novel approach to solve the evaluation problem of medical service quality including a criteria framework for the evaluation of medical service quality and a novel MCDM method. What’s more, aiming at eliminating the discordance between decision information and weight vector of criteria determined by decisionmakers (DMs), this paper proposes the concept of cross-entropy and knowledge measure in IVq-RDHF environment to extract weight vector from DMs’ decision information. Finally, this paper presents a numerical example of the evaluation of medical service for hospitals to illustrate the availability of the novel method and compares our method with other MCDM methods to demonstrate the superiority of our method. According to the comparison result, our method has more advantages than other methods.

OWA operators in the weighted average and their application in decision making

100%

Merigó J. M.

Control and Cybernetics

2012

tom Vol. 41, no. 3

605-643

We introduce a new aggregation operator that unifies the weighted average (WA) and the ordered weighted averaging (OWA) operator in a single formulation. We call it the ordered weighted averaging - weighted average (OWAWA) operator. This aggregation operator provides a more complete representation of the weighted average and the OWA operator because it considers the degree of importance that each concept has in the aggregation and includes them as particular cases of a more general context. We study different properties and families of the OWAWA operator. The applicability of this method is very broad because any study that uses the weighted average or the OWA can be revised and extender with our approach. We focus on a multi-person decision-making application in the selection of financial strategies.

Minkowski’s inequality based sensitivity analysis of fuzzy signatures

88%

Harmati I. Á. , Bukovics Á. , Kóczy L. T.

2016

tom Vol. 6, No. 4

219--229

Fuzzy signatures were introduced as special tools to describe and handle complex systems without their detailed mathematical models. The input parameters of these systems naturally have uncertainties, due to human activities or lack of precise data. These uncertainties influence the final conclusion or decision about the system. In this paper we discuss the sensitivity of the weigthed general mean aggregation operator to the uncertainty of the input values, then we analyse the sensitivity of fuzzy signatures equipped with these aggregation operators. Finally, we apply our results to a fuzzy signature used in civil enginnering.

Dealing with Imprecise Knowledge on Preferences and Majority in Group Decision Making: Towards a Unified Characterization of Individual and Collective Choice Functions

88%

Kacprzyk J. , Zadrożny S.

2003

tom Vol. 51, nr 3

279-302

A fuzzy preference relation is a popular model to represent both individual and group preferences. However, what is often sought is a subset of alternatives that is an ultimate solution of a decision problem. In order to arrive at such a final solution individual and/or group choice rules may be employed. There is a wealth of such rules devised in the context of the classiccal, crisp preference relations. Originally, most of the popular group decision making rules were conceived for classical (crisp) prefernce relations (orderings), and then extended to the case of traditional fuzzy preference relations. Moreover, they often differ in their assumptions about the properties of the preference relations to be processed. In the paper we pursue the path towards a universal representation of such rules that provides an effective generalization of the classical rules for the fuzzy case. Moreover, it leads to a meaningful extension to the linguistic preferences, in the spirit of the computing with words paradigm.