Combining numerical and linguistic information in group decision making

Privacy and Trust in eHealth: A Fuzzy Linguistic Solution for Calculating the Merit of Service

The use of eHealth and healthcare services are becoming increasingly common across networks and ecosystems. Identifying the quality and health impact of these services is a big problem that in many cases it is difficult determine. Health ecosystems are seldom designed with privacy and trust in mind, and the service user has almost no way of knowing how much trust to place in the service provider and other stakeholders using his or her personal health information (PHI). In addition, the service user cannot rely on privacy laws, and the ecosystem is not a trustworthy system. This demonstrates that, in real life, the user does not have significant privacy. Therefore, before starting to use eHealth services and subsequently disclosing personal health information (PHI), the user would benefit from tools to measure the level of privacy and trust the ecosystem can offer. For this purpose, the authors developed a solution that enables the service user to calculate a Merit of Service (Fuzzy attractiveness rating (FAR)) for the service provider and for the network where PHI is processed. A conceptual model for an eHealth ecosystem was developed. With the help of heuristic methods and system and literature analysis, a novel proposal to identify trust and privacy attributes focused on eHealth was developed. The FAR value is a combination of the service network’s privacy and trust features, and the expected health impact of the service. The computational Fuzzy linguistic method was used to calculate the FAR. For user friendliness, the Fuzzy value of Merit was transformed into a linguistic Fuzzy label. Finally, an illustrative example of FAR calculation is presented.

Fuzzy logic programming and adaptable design of medical products for the COVID-19 anti-epidemic normalization

BACKGROUND

The COVID-19 prevention and control constantly affects lives worldwide. In this paper, household medical products were analyzed using fuzzy logic. Considering the household anti-epidemic status, economic and environmental benefits, the adaptable design method of anti-epidemic products in the vestibule was proposed. The measure of adaptable design method still have shortcomings. Therefore, an improved method that is based on fuzzy logic programming is required.

METHOD

Firstly, common medical product types used in vestibules and household anti-epidemic products were identified and summarized into product sets. Then matching degree matrix was obtained by functional configuration decomposition and matching calculations. Secondly, experts were invited to evaluate the paired comparative probability matrices and linguistic variables, and the evaluation data were converted by trapezoidal membership functions, fuzzy numbers and the defuzzification method to obtain the usage probability values (PR) for product functions. Finally, the matching degree value (P) and the product function (PF) were calculated by adaptability measure formula, and product function, the adaptability factor and the adaptability (A) were obtained.

RESULTS AND DISCUSSION

Our results show that the degree of adaptability of each product function in the product set from PF₁ to PF₁₀can be evaluated. Based on the principles of sorting of values from high to low, the top five PF (n = 10) for P value is PF₁₀, PF₅, PF₆, PF₈ and PF₁; The top five PF for P value is PF₂, PF₁, PF₃, PF₇ and PF₈; The top five PF for A value is PF₂(0.242), PF₁(0.232), PF₅(0.225), PF₈(0.222) and PF₃(0.221). These values allow us to summarize and draw visual charts according to the above data sorting mode. The higher the value of the product function, the more it can be prioritized for design development with functional cost savings, simplification or clustering.

CONCLUSION

This study proposes an adaptable design method based on fuzzy logic programming. The data results in this study can guide the development and programming of the vestibule anti-epidemic products. The higher adaptability value of a product function indicates that it is more capable of being simplified, clustered, and adapting to changes in the product set.

Construction of Quality Indicators Based on Pre-Established Goals: Application to a Colombian Public University

This study creates indicators of adequacy and excellence based on multiple-criteria decision-making (MCDM) methods and fuzzy logic. The calculation of indicators presents two main difficulties: The nature of the data (numerical, interval, and linguistic values are mixed) and the objective of each criterion (which does not have to reach either the maximum or the minimum). A method is proposed, based on similarity measures with predetermined ideals, that is capable of overcoming these difficulties to provide easy-to-interpret information about the quality of the alternatives. To illustrate the usefulness of this proposed method, it has been applied to data collected from students across nine semesters at the Bucaramanga campus of the Industrial University of Santander in Colombia. This case study demonstrates that the proposed method can facilitate strategic decisions at an institution and open the way for the establishment of action policies regarding gender inequality and economic disparity, among other things.

Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure

Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions.

A Novel Hybrid Fuzzy Grey TOPSIS Method: Supplier Evaluation of a Collaborative Manufacturing Enterprise

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

Some Hesitant Fuzzy Hamacher Power-Aggregation Operators for Multiple-Attribute Decision-Making

As an extension of the fuzzy set, the hesitant fuzzy set is used to effectively solve the hesitation of decision-makers in group decision-making and to rigorously express the decision information. In this paper, we first introduce some new hesitant fuzzy Hamacher power-aggregation operators for hesitant fuzzy information based on Hamacher t-norm and t-conorm. Some desirable properties of these operators is shown, and the interrelationships between them are given. Furthermore, the relationships between the proposed aggregation operators and the existing hesitant fuzzy power-aggregation operators are discussed. Based on the proposed aggregation operators, we develop a new approach for multiple-attribute decision-making problems. Finally, a practical example is provided to illustrate the effectiveness of the developed approach, and the advantages of our approach are analyzed by comparison with other existing approaches.

An interactive group decision model for selecting treatment schemes for mitigating air pollution

Air pollution has caused huge losses of life and property. So, how to choose a practically effective scheme to m.itigate air pollution is of great significance. However, such a selection problem of treatment schemes represents really a group negotiation process of many decision makers (DMs), involving a variety of fuzzy information and preferences. To successfully address this selection problem, this paper proposes a novel group negotiation decision model by jointly employing various approaches, such as hesitant fuzzy set, grey target, grey incidence analysis, and graph model for conflict resolution (GMCR). Then, this model is used to determine the equilibrium schemes for treating air pollution. It is expected that this work provides a method for Chinese government to introduce programs to target air pollution control.

Development of a mathematical model to evaluate the rate of aggregate risk of invasive alien plant species: Fuzzy risk assessment approach

This study presents a novel approach to evaluate the rate of aggregate risk of Invasive Alien Plant Species. Using risk values and grade of importance of weights of risk factors which may reflect invasiveness of plant species are considered. We use Linguistic Ordered Weighted Averaging operator to evaluate the grade of important of weights. Since the risk values and important weights are identified from two different linguistic term sets, fuzzy set theory techniques were used to combine the two sets. The rates obtained from the model were compared with NRA risk levels and the model was validated with data from known and non-invasive species. The model is improved by weighting the risk values of risk factors. The improved model produced significant results and resulted a better tracking system for identifying potential invaders than the conventional risk assessment.

Similarity-Based Approach for Group Decision Making with Multi-Granularity Linguistic Information

The aim of this article is to investigate the approach for multi-attribute group decision-making, in which the attribute values take the form of multi-granularity multiplicative linguistic information. Firstly, to process multiple sources of decision information assessed in different multiplicative linguistic label sets, a method for transforming multi-granularity multiplicative linguistic information into multiplicative trapezoidal fuzzy numbers is proposed. Then, a formula for ranking multiplicative trapezoidal fuzzy numbers is given based on geometric mean. Furthermore, the concept of similarity degree between two multiplicative trapezoidal fuzzy numbers is defined. The attribute weights are obtained by solving some optimization models. An effective approach for group decision making with multi-granularity multiplicative linguistic information is developed based on the ordered weighted geometric mean operator and proposed formulae. Finally, a practical example is provided to illustrate the practicality and validity of the proposed method.

POWER GEOMETRIC OPERATORS FOR GROUP DECISION MAKING UNDER MULTIPLICATIVE LINGUISTIC PREFERENCE RELATIONS

The aim of this paper is to develop some new linguistic aggregation operators, such as linguistic power geometric (LPG) operator, linguistic weighted PG operator, LPOWG operator which are based on PG operator. We have studied some desired properties of the developed operators, such as commutativity, idempotency, boundary, etc. Moreover, we have developed two approaches to deal with group decision making problems under multiplicative linguistic preference relations. If the weighting vector of the decision makers is known, we develop an approach which is based on the linguistic weighted PG operator. On the other hand, if the weighting vector of the decision makers is unknown, we develop another approach which is based on the LPOWG. Finally, a practical example is given to illustrate the multiple attribute group decision making process; a comparative study to the linguistic weighted geometric average (LWGA) operator method is also demonstrated.