Theory of fuzzy integrals and its applications

A novel spherical fuzzy QFD method and its application to the linear delta robot technology development

A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development

This paper presents a novel approach for evaluating the smart medical device selection process in a group decision-making setting in an uncertain decision environment. Intuitionistic fuzzy Choquet integral (IFCI) approach is applied to treat the uncertainty and vagueness in the decision-making process. IFCI also considers the interactions among the decision criteria in the data provided by the decision makers. In this paper, the emphasis is placed upon the selection of wearable monitoring devices for cardiac patients. The goal is to present the complexity of the problem, raise interest among specialists in the healthcare industry and assess smart medical devices under different evaluation criteria. The problem is formulated as a multi-criteria decision model with ten criteria and eight alternatives. The results of the IFCI model are analyzed using 9 sensitivity analysis scenarios, which prove the adequacy of the obtained results. The result of the proposed method is also compared with the IF extensions of the VIKOR, TOPSIS, COPRAS, MOORA and MULTIMOORA models in order to validate and verify the obtained outcome. The Spearman coefficient of correlation is applied to check the stability of the variations in the rankings. The results indicate that the model and the rankings it generates are sufficiently stable.

Smart medical device selection based on intuitionistic fuzzy Choquet integral

• An approach is proposed to analyze the interactions between the criteria. • A new evaluation framework for ranking logistic autonomous vehicles is introduced. • A new group decision model based on Choquet Integral Spherical fuzzy is proposed. • The Choquet Integral is expanded with spherical fuzzy to increase its power. • Evaluate and select the most appropriate and best logistics autonomous vehicles. Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios. 

Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach

In multiple attribute decision making (MADM), hesitant fuzzy sets (HFSs) are powerful tools for expressing uncertain and vague information. Recently, MADM problems with hesitant fuzzy information have attracted increasing attention, and many MADM methods have been developed. However, only a limited amount of research has considered MADM problems that simultaneously determine attribute weights and decision-maker (DM) preferences. Therefore, we propose an MADM approach for such problems under a hesitant fuzzy environment. First, we derive extended distance and correlation coefficient measures for HFSs that are more reasonable and effective when the DM preferences are considered. We then apply the extended distance measure to subjective and objective preference information to determine attribute weights, and use these to calculate the weighted correlation coefficient between the ideal choice and each alternative. Further, we determine the ranking order of all alternatives, from which it is easy to identify the best choice. Finally, we present an example that demonstrates the practicality of the proposed approach.

MADM based on distance and correlation coefficient measures with decision-maker preferences under a hesitant fuzzy environment

As a customer-oriented methodology, fuzzy quality function deployment (QFD) has been widely applied to translate customer requirements into product design requirements and to improve the product quality in fuzzy environments. This paper puts forward an approach, which rates technical attributes in fuzzy QFD by integrating interval-valued intuitionistic fuzzy sets and Choquet integral under the case of considering the correlation among customer requirements. A method for converting interval-valued intuitionistic fuzzy numbers into relative weights for customer requirements is proposed. In order to reflect different attitudes toward risks from different decision makers, a score function with a degree of risk preference K and interval comparison matrices are obtained. Finally, the proposed approach is illustrated with a scenario about the process of designing steering wheel for electric vehicles.

Technical attributes ratings in fuzzy QFD by integrating interval-valued intuitionistic fuzzy sets and Choquet integral

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MADM based on distance and correlation coefficient measures with decision-maker preferences under a hesitant fuzzy environment

Technical attributes ratings in fuzzy QFD by integrating interval-valued intuitionistic fuzzy sets and Choquet integral