Search

Aggregation methods in group decision-making refer to techniques used to combine the individual preferences, opinions, or judgments of group members into a collective decision. Each aggregation method has its advantages and disadvantages, and the best method to use depends on the specific situation and the goals of the decision-making process. In certain cases, final rankings of alternatives in the decision-making process may depend on the way of combining different attitudes. The focus of this paper is the application and comparative analysis of the aggregation operators, specifically, arithmetic mean (AM), geometric mean (GM), and Dombi Bonferroni mean (DBM), to the process of criteria weights determination in a fuzzy environment. The criteria weights are determined using Fuzzy Multi-Criteria Decision-Making (F-MCDM) methods, such as Fuzzy Analytic Hierarchy Process (F-AHP), Fuzzy Pivot Pairwise Relative Criteria Importance Assessment (F-PIPRECIA), and Fuzzy Full Consistency Method (F-FUCOM), while the final alternative ranking is obtained by Fuzzy Weighted Aggregated Sum Product Assessment (F-WASPAS). A comparison of aggregation operators is done for the real case of location selection problem for a used motor oil transfer station in the regional center of Southern and Eastern Serbia, the city of Niš. The results obtained in this study showed that the views of different experts and application of a certain aggregation approach may have a significant impact on the values of criteria weight coefficients and further on the final ranking of alternatives. This paper is expected to stimulate future research into the impact of aggregation methods on final rankings in the decision-making process, especially in the field of waste management.

Many aggregation operators are studied to deal with multi-criteria group decision-making problems. Whenever information has two aspects, intuitionistic fuzzy sets and Pythagorean fuzzy sets are employed to handle the information. However, q-rung orthopair fuzzy sets are more flexible and suitable because they cover information widely. The current paper primarily focuses on the multi-criteria group decision-making technique based on prioritization and two robust aggregation operators based on Aczel–Alsina t-norm and t-conorm. This paper suggests two new aggregation operators based on q-rung orthopair fuzzy information and Aczel–Alsina t-norm and t-conorm, respectively. Firstly, novel q-rung orthopair fuzzy prioritized Aczel–Alsina averaging and q-rung orthopair fuzzy prioritized Aczel–Alsina geometric operators are proposed, involving priority weights of the information. Several related results of the proposed aggregation operators are investigated to see their diversity. A multi-criteria group decision-making algorithm based on newly established aggregation operators is developed, and a comprehensive numerical example for the selection of the most suitable energy resource is carried out. The proposed aggregation operators are compared with other operators to see some advantages of the proposed work. The proposed aggregation operators have a wider range for handling information, with priority degrees, and are based on novel Aczel–Alsina t-norm and t-conorm.

Many fuzzy concepts have been researched and described with uncertain information. Collecting data under uncertain information is a difficult task, especially when there is a difference between the opinions of experts. To deal with such situations, different types of operators have been introduced. This paper aims to develop the Maclaurin symmetric mean (MSM) operator for the information in the shape of the interval-valued spherical fuzzy set (IVSFS). In this article, a family of aggregation operators (AOs) is proposed which consists of interval valued spherical fuzzy Maclaurin symmetric mean operator (IVSFMSM), interval valued spherical fuzzy weighted Maclaurin symmetric mean (IVSFWMSM), interval valued spherical fuzzy dual Maclaurin symmetric mean (IVSFDMSM), and interval valued spherical fuzzy dual weighted Maclaurin symmetric mean (IVSFDWMSM) operators. In this paper, we studied an elucidative example to discuss the evaluation of multi-national companies for the application of the proposed operator. Then the obtained results from the proposed operators are compared. The results obtained are graphed and tabulated for a better understanding.

The neutrosophic sets and numbers have an important role in modeling the problems. Recently, studies on neutrosophic numbers and single-valued neutrosophic numbers which is a subclass of neutrosophic numbers have increased, rapidly. Cauchy distribution is an important concept in the statistic. In this paper, the notion of Cauchy single-valued neutrosophic numbers (CSVNNs) and α−cuts are introduced based on the Cauchy distribution formula. Summation, multiplication, and division operations between two CSVNNs are defined and given related examples. Also, the score functions of CSVNNs, arithmetic and geometric aggregation operators of them are described. Based on the defined new concepts, a multi-attribute group decision-making method is developed. Finally, to illustrate how the proposed method works, an application of the proposed method in the selection of a project to be supported and funded is developed. In this method, for each of the criteria, different score functions are determined by using the aggregation operators and score functions of the CSVNNs. Then, evaluations of the decision-makers are transformed into new values under the derived score functions for the decision. In applications, in general, decision-makers assign to criteria some values between 0 and 1 directly. In the method proposed in this paper, weights of the criteria are considered as the different functions. Therefore this method presents a more general perspective on decision-making problems.&nbsp

Decision-makers' subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility. © 2022, The Author(s). This paper is supported by Jiangsu Social Science Foundation with grant 18JD009. This paper is supported by grant APVV-18-0052.

Tools for decision making need to be simple to use. In previous papers, we advocated that decision engineering needs to provide these tools, as well as a list of necessary properties that aggregation functions need to satisfy. When we model decisions using aggregation functions, andness-directedness is one of them. A crucial aspect in any decision is the degree of compromise between criteria. Given an aggregation function, andness establishes to what degree the function behaves in a conjunctive manner. That is, to what degree some criteria are mandatory. Nevertheless, from an engineering perspective, what we know is that some criteria are strongly required and we cannot ignore a bad evaluation even when other criteria are correctly evaluated. That is, given our requirements of andness, what are the aggregation functions we need to select. Andness is not only for mean-like functions, but it also applies to t-norms and t-conorms. In this paper, we study this problem and show how to select t-norms and t-conorms based on the andness level.

Information management is a common paradigm in modern decision-making. A wide range of decision-making techniques have been proposed in the literature to model complex business processes. In this Special Issue, 16 selected and peer-reviewed original research articles contribute to business information management in various current real-world problems by proposing crisp or uncertain multiple-criteria decision-making (MCDM) models and techniques, mostly including multi-attribute decision-making (MADM) approaches in addition to a single paper proposing an interactive multi-objective decision-making (MODM) approach. The papers are mainly concentrated in three application areas: supplier selection and rational order allocation, the evaluation and selection of goods or facilities, and personnel selection/partner selection. A number of new approaches are proposed that are expected to attract great interest from the research community., This article belongs to the Special Issue Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management

Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks.

The picture fuzzy set is a generation of an intuitionistic fuzzy set. The aggregation operators are important tools in the process of information aggregation. Some aggregation operators for picture fuzzy sets have been proposed in previous papers, but some of them are defective for picture fuzzy multi-attribute decision making. In this paper, we introduce a transformation method for a picture fuzzy number and trapezoidal fuzzy number. Based on this method, we proposed a picture fuzzy multiplication operation and a picture fuzzy power operation. Moreover, we develop the picture fuzzy weighted geometric (PFWG) aggregation operator, the picture fuzzy ordered weighted geometric (PFOWG) aggregation operator and the picture fuzzy hybrid geometric (PFHG) aggregation operator. The related properties are also studied. Finally, we apply the proposed aggregation operators to multi-attribute decision making and pattern recognition.

In this paper, we first introduce the concept of T-spherical hesitant fuzzy set (T-SHFS) on the basis of the combination of T-spherical fuzzy sets (T-SFSs) and hesitant fuzzy sets (HFSs). This model can provide more accuracy in expressing fuzzy and indeterminate data. Then, we develop the basic operational laws of T-SHFSs and study their properties. Also, we propose the T-spherical hesitant fuzzy weighted averaging (T-SHFWA) operator and the T-spherical hesitant fuzzy weighted geometric (T-SHFWG) operator and investigate their properties. Furthermore, two newly approaches to multi attribute decision making within the framework of T-SHFS are developed on the basis of the proposed aggregation operators. An illustrative example is given to demonstrate the effectiveness of the developed approaches in dealing with uncertain and indeterminate decision making problems. Finally, the proposed models’ performance is evaluated to the existing models. Results from this comparison analysis reveal a great similarity and consistency while using other models.

In this paper we describe a new automatic partitioning method and a first order weighted fuzzy time series forecasting model. First, we show that our automatic fuzzy partitioning method provides an accurate approximation to the original time series. The fuzzy sets extracted from our partitioning are grouped to create a rule-base that will be used in forecasting. We found that the accuracy of our first order model is improved when an ordered weighting averaging operator is applied. The model presented in this paper does not attempt to produce the most accurate forecasting results, when compared with other more complex higher order models. Our goal is to show that there is still space for improvement when simple first order forecasting models are used. Our results show that the combination of a simple partitioning method, a first order model, and an averaging operator is still capable of outperforming not only the first order models that have proposed in the literature but also other higher order models.

Motivated by a specific decision-making situation, this work proposes the concept and definition of unsymmetrical basic uncertain information which is a further generalization of basic uncertain information and can model uncertainties in some new decision-making situations. We show that unsymmetrical basic uncertain information in some sense can model linguistic hedges such as 'at least' and 'at most'. Formative weighted arithmetic means and induced aggregations are defined for the proposed concept. Rules-based decision making and semi-copula based integral for this concept with some numerical examples are also presented. The paper is supported by National Natural Science Foundation of China with grant nos. 72171182 and 71801175, Grant APVV-18-0052, Grant PID2019-108392GBI00, financed by MCIN/AEI/10.13039/501100011033.

The principal purpose of this paper is to analyze different methods for decision making, with a focus on entrepreneurship in Latin America. Decision-making methods may be informed by aggregation operators that are based on the use of probabilities, weighted averages (WAs) and generalized aggregation operators. The paper presents a new generalized probabilistic weighted averaging (GPWA) operator that unifies WAs and probability in the same formulation, considering the degree of importance of each concept used in the analysis. The fundamental advantage of this approach is that it includes a wide range of particular cases including the probabilistic weighted averaging (PWA) operator, the probabilistic weighted geometric averaging (PWGA) operator and the probabilistic weighted quadratic averaging (PWQA) operator. Quasi-arithmetic means are used to obtain the Quasi-PWA operator and to generalize the approach, which is then applied to a set of hypothetical entrepreneurial investment decisions in a politically unified Latin American region., El objetivo principal de este artículo es analizar diferentes mé- todos de toma de decisiones, con enfoque en el emprendimiento en Latinoamérica. Los métodos de toma de decisiones pueden recibir información por parte de operadores de agregación basados en el uso de probabilidades, promedios ponderados (PP) y operadores de agregación generalizados. El artículo presenta un nuevo operador probabilístico generalizado de promedios ponderados (GPWA) que unifica los promedios ponderados y la probabilidad en la misma formulación, considerando el grado de importancia de cada concepto usado en el análisis. La ventaja fundamental de este enfoque es que incluye un amplio rango de casos particulares, incluyendo el operador probabilístico de promedios ponderados, el operador probabilístico de promedios geométricos ponderados y el operador probabilístico de promedios cuadráticos ponderados. Se emplean medios cuasiaritméticos para obtener el operador cuasiprobabilístico de promedios ponderados y para generalizar el enfoque, que luego es aplicado a un conjunto de decisiones empresariales hipotéticas en cuanto a inversión en una región latinoamericana unificada políticamente.

The linguistic interval-valued Pythagorean fuzzy (LIVPF) sets, which absorb the advantages of linguistic terms set and interval-valued Pythagorean fuzzy sets, can efficiently describe decision makers’ evaluation information in multi-attribute group decision-making (MAGDM) problems. When investigating aggregation operators of linguistic interval-valued Pythagorean fuzzy (LIVPF) information, we have to consider two important issues, viz. the operational rules of LIVPF numbers and aggregation functions. The classical Archimedean t-norm and t-conorm (ATT) are a famous t-norm and t-conorm, which can produce some special cases. Recently, ATT has been widely applied in different fuzzy decision-making information. Hence, in this paper, for the first issue, we propose some novel operational rules of LIVPF numbers based on ATT. The new operational laws are flexible and can generate some useful operations. For the second issue, we choose a powerful function, i.e., the extended power average (EPA) operator as the aggregation function. The prominent advantages of EPA are that it not only considers the relationship among input arguments, but also dynamically changes the weights of input arguments by employing a parameter. Hence, our proposed novel aggregation operators for LIVPFNs are flexible and is suitable to handle MAGDM problems in actual life. Afterward, we further present a novel MAGDM method under LIVPF conditions. The main finding of our study is a new MAGDM method, which is more powerful and flexible than existing ones. Finally, we apply the method in a sustainable building materials selection to show its effectiveness. Additionally, comparison analysis is provided to demonstrate the advantages and superiorities of the proposed method.

In this paper, appropriate least-squares methods were developed to operate in data fusion scenarios. These methods generate optimal estimates by combining measurements from a finite collection of samples. The aggregation operators of the average type, namely, ordered weighted averaging (OWA), Choquet integral, and mixture operators, were applied to formulate the optimization problem. Numerical examples about fitting curves to a given set of points are provided to show the effectiveness of the proposed algorithms.

To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied., The authors would like to thank FEDER financial support from the Projects TIN2013-40658-P and TIN2016- 75850-P.

[EN] The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of operators between the minimum and the maximum. This paper presents the OWA weighted average distance operator. The main advantage of this new approach is that it unifies the weighted Hamming distance and the OWA distance in the same formulation and considering the degree of importance that each concept has in the analysis. This operator includes a wide range of particular cases from the minimum to the maximum distance. Some further generalizations are also developed with generalized and quasi-arithmetic means. The use of Bonferroni means under this framework is also studied. The paper ends with an application of the new approach in a group decision making problem with Dempster-Shafer belief structure regarding the selection of strategies., We would like to thank the editor-in-chief, the associate editor and the anonymous reviewers for valuable comments that have improved the quality of the paper. Support from the project PIEF-GA-2011-300062 from the European Commission and from the University of Chile is gratefully acknowledged

Simple Summary COVID-19 is a rapidly spreading and mutating pandemic. In the case of some people the disease can be fatal It has been observed that weight and age are parameters of comorbidity If it is difficult to take into account the uncertainties related to the combination of the two parameters to build up a model of simulation. Therefore, we propose in this article a SIR/SIH model with fuzzy parameters which allows us to simulate the pandemic in the absence of barrier actions and vaccines. Abstract In this paper, we propose a multi-group SIR to simulate the spread of COVID-19 in an island context. The multi-group aspect enables us to modelize transmissions of the virus between non-vaccinated individuals within an age group as well as between different age groups. In addition, fuzzy subsets and aggregation operators are used to account for the increased risks associated with age and obesity within these different groups. From a conceptual point of view, the model emphasizes the notion of Hospitalization which is the major stake of this pandemic by replacing the compartment R (Removed) by compartment H (Hospitalization). The experimental results were carried out using medical and demographic data from the archipelago, Guadeloupe (French West Indies) in the Caribbean. These results show that without the respect of barrier gestures, a first wave would concern the elderly then a second the adults and the young people, which conforms to the real data.

Aggregation operators perform a valuable and significant role in various decision-making processes. Averaging and geometric aggregation operators are both used for capturing the interrelationships of the aggregated preferences, even if the preferences are independent. The purpose of this paper is to analyze the theory of complex linguistic fuzzy (CLF) sets and their important laws, such as algebraic laws, score values, and accuracy values, and to describe the relationship between the score and accuracy values with the help of their properties. Additionally, based on the proposed CLF information, we introduce the theory of CLF weighted averaging (CLFWA), CLF ordered weighted averaging (CLFOWA), CLF hybrid averaging (CLFHA), CLF weighted geometric (CLFWG), CLF ordered weighted geometric (CLFOWG), and CLF hybrid geometric (CLFHG) operators. The fundamental properties and some valuable results of these operators are evaluated, and their particular cases are described. Based on the presented operators, a technique for evaluating the “multi-attribute decision-making” (MADM) problems in the consideration of CLF sets is derived. The superiority of the derived technique is illustrated via a practical example, a set of experiments, and significant and qualitative comparisons. The illustration results indicate that the derived technique can be feasible and superior in evaluating CLF information. Further, it can be used for determining the interrelationships of attributes and the criteria of experts. Moreover, it is valuable and capable of evaluating the MADM problems using CLF numbers.

The key objective of the proposed work in this paper is to introduce a generalized form of linguistic picture fuzzy set, so-called linguistic spherical fuzzy set (LSFS), combining the notion of linguistic fuzzy set and spherical fuzzy set. In LSFS we deal with the vague and defective information in decision making. LSFS is characterized by linguistic positive, linguistic neutral and linguistic negative membership degree which satisfies the conditions that the square sum of its linguistic membership degrees is less than or equal to 1. In this paper, we investigate the basic operations of linguistic spherical fuzzy sets and discuss some related results. We extend operational laws of aggregation operators and propose linguistic spherical fuzzy weighted averaging and geometric operators based on spherical fuzzy numbers. Further, the proposed aggregation operators of linguistic spherical fuzzy number are applied to multi-attribute group decision-making problems. To implement the proposed models, we provide some numerical applications of group decision-making problems. In addition, compared with the previous model, we conclude that the proposed technique is more effective and reliable.

Ambiguous and uncertain facts can be handled using a hesitant 2-tuple linguistic set (H2TLS), an important expansion of the 2-tuple linguistic set. The vagueness and uncertainty of data can be grabbed by using aggregation operators. Therefore, aggregation operators play an important role in computational processes to merge the information provided by decision makers (DMs). Furthermore, the aggregation operator is a potential mechanism for merging multisource data which is synonymous with cooperative preference. The aggregation operators need to be studied and analyzed from various perspectives to represent complex choice situations more readily and capture the diverse experiences of DMs. In this manuscript, we propose some valuable operational laws for H2TLS. These new operational laws work through the individual aggregation of linguistic words and the collection of translation parameters. We introduced a hesitant 2-tuple linguistic weighted average (H2TLWA) operator to solve multi-criteria group decision-making (MCGDM) problems. We also define hesitant 2-tuple linguistic Bonferroni mean (H2TLBM) operator, hesitant 2-tuple linguistic geometric Bonferroni mean (H2TLGBM) operator, hesitant 2-tuple linguistic Heronian mean (H2TLHM) operator, and a hesitant 2-tuple linguistic geometric Heronian mean (H2TLGHM) operator based on the novel operational laws proposed in this paper. We define the aggregation operators for addition, subtraction, multiplication, division, scalar multiplication, power and complement with their respective properties. An application example and comparison analysis were examined to show the usefulness and practicality of the work.

Literature of aggregation operators defined in the domains of linguistic and interval-valued linguistic information is extensive and vast. But, some operators are not well-defined and do not benefit the researchers. In this paper, we have highlighted the flaws in defining the operators and demonstrated them via examples. Further, some remedial suggestions for the improvement of the definitions are given.

Since its initiation, hesitant fuzzy sets (HFSs) have gained prominence thanks to their capability to describe the hesitation of experts to assign membership degrees to objects belonging to a concept. Proportional hesitant fuzzy sets (PHFSs) are an important extension of HFSs and are characterized by the combination of possible membership degrees and their associated proportional information. PHFSs have a huge application potential for hesitant fuzzy GDM problems, because the proportional information in PHFSs can be determined objectively and the introduction of this new information dimension can effectively reduce the uncertainty. Nevertheless, PHFSs have not yet attracted sufficient attention from researchers and practitioners, which motivates us to expand the theory of PHFSs and explore its application potential. The main work comprises the following three aspects: First, we define some basis operations on PHFSs, develop aggregation operators for PHFSs, and demonstrate their properties and interrelationships to lay the theoretical foundations for the application of PHFSs. Next, we construct two multicriteria group decision making (MCGDM) models based on the proposed PHFS-based aggregation operators to bridge between theory and practice for PHFSs. In this step, we propose a method for transforming HFSs or fuzzy sets (FSs) into PHFSs, and two methods based on the maximum entropy principle are proposed for specifying criterion weights. Finally, we investigate a practical case study of the problem of selecting an electric vehicle battery (EVB) supplier to validate the outstanding advantages of PHFSs, explore the compensation characteristics and the applicability of the PHFS-based aggregation operators, and demonstrate the effectiveness and feasibility of the proposed MCGDM models. This paper provides a useful reference for MCGDM in a hesitant fuzzy context.

In distributed processing systems it is often necessary to coordinate the allocation of shared resources that should be assigned in the processes in the modality of mutual exclusion; in such cases, the order in which the shared resources will be assigned in the processes that require them must be decided; in this paper we propose an aggregation operator (which could be used by a shared resources manager module) that will decide the order of allocation of the resources to the processes considering the requirements of the processes (shared resources) and the state of the distributed nodes where the processes operate (their computational load).

Andness directed aggregation is about selecting aggregators from a desired andness level. In this paper we consider operators of the OWA and WOWA families: aggregation functions that permit us to represent some degree of compensation of the input values. In addition to compensation, WOWA permits us to represent importance (weights) of the input values. Selection of appropriate parameters given an andness level will be based on families of fuzzy quantifiers. CC BY 4.0Available online 14 September 2020

La calidad del software, como sistema de información, está frecuentemente determinada por la habilidad para satisfacer las necesidades de los clientes y usuarios finales, obtenidas y especificadas como requisitos de software. Para satisfacer estas necesidades resulta importante la realización de una correcta ingeniería de requisitos y especialmente su priorización. Tal priorización de requisitos de software es un proceso complejo de toma de decisiones. Los enfoques tradicionales no ejecutan la agregación de los criterios con la suficiente flexibilidad y adaptabilidad a los contextos específicos de las organizaciones. En el presente trabajo se propone un método de priorización de requisitos, que hace uso de los operadores de agregación, para la fusión de la información, y el método de jerarquía analítica para determinar la importancia de los criterios. La propuesta permite la inclusión de aspectos tales como la importancia de los criterios y nivel de compensación. Un estudio de caso muestra aplicabilidad de la propuesta se desarrolla un estudio de caso. El artículo finaliza con propuestas de trabajos futuros que contribuyan a la aplicabilidad del método., A key goal of any engineering and software engineering in particular, is the quality of the final product. Software quality as an information system is often determined by the ability to meet the needs of customers and end users, as obtained as software requirements. To satisfy that needs is important a correct requirement engineering process in general, and a correct requirement prioritization in particular: Prioritizing software requirements is a complex decision making process. Traditional approaches do not perform aggregation of criteria with sufficient flexibility and adaptability lo the specific contexts of organizations. In this paper we propose a requirements prioritization method that uses aggregation operators far information fusion and the analytic hierarchy process. The proposal allows the inclusion of aspects such as the importance of the criteria and optimist/pessimism level. To demonstrate the applicability of the proposal case study is developed. The paper ends with further work recommendations for extending the method.

The paper first summarizes the orness measures and their common characteristics of some averaging operators: the quasi-arithmetic mean, the ordered weighted averaging (OWA) operator, the regular increasing monotone (RIM) quantifier and the weighted function average operator, respectively. Then it focuses on the aggregation properties and operator determination methods for two kinds of quasi-arithmetic mean-based compound aggregation operators: the quasi-OWA (ordered weighted averaging) operator and the Bajraktarević mean. The former is the combination of the quasi-arithmetic mean and the OWA operator, while the latter is the combination of the quasi-arithmetic mean and the weighted function average operator. Two quasi-OWA operator forms are given, where the OWA operator is assigned directly or generated from a RIM (regular increasing monotone) quantifier indirectly. The orness indexes to reflect the or-like level of the quasi-OWA operator and Bajraktarević mean are proposed. With generating function techniques, the properties of the quasi-OWA operator and Bajraktarević mean are discussed to show the rationality of these orness definitions. Based on these properties, two families of parameterized quasi-OWA operator and Bajraktarević mean with exponential and power function generators are proposed and compared. It shows that the method of this paper can also be applied to other function-based aggregation operators.

This paper elaborates the different methods to generate normalized weight vector in multi-criteria decision-making where the given information of both criteria and inputs are uncertain and can be expressed by basic uncertain information. Some general weight allocation paradigms are proposed in view of their convenience in expression. In multi-criteria decision-making, the given importance for each considered criterion may have different extents of uncertainty. Accordingly, we propose some special induced weight-allocation methods. The inputs can be also associated with varying uncertainty extents, and then we develop several induced weight-generation methods for consideration. In addition, we present some suggested and prescriptive weight allocation rules and analyze their reasonability.

In this paper, the novel approach of complex T-spherical fuzzy sets (CTSFSs) and their operational laws are explored and also verified with the help of examples. CTSFS composes the grade of truth, abstinence, and falsity with a condition that the sum of q-power of the real part (also for imaginary part) of the truth, abstinence, and falsity grades cannot be exceeded from a unit interval. Additionally, to examine the interrelationships among the complex T-spherical fuzzy numbers (CTSFNs), we propose two aggregation operators, called complex T-spherical fuzzy weighted averaging (CTSFWA) and complex T-spherical fuzzy weighted geometric (CTSFWG) operators. A multi-attribute decision making (MADM) problem is resolved based on CTSFNs by using the proposed CTSFWA and CTSFWG operators. To examine the proficiency and reliability of the explored works, we use an example to make comparisons between the proposed operators and some existing operators. Based on the comparison results, the proposed CTSFWA and CTSFWG operators are well suited in the fuzzy environment with legitimacy and prevalence by contrasting other existing operators.

Hesitant neutrosophic sets can accomodate more uncertainty compare to hesitant fuzzy sets and hesitant intuitionistic sets. On the other hand, triangular neutrosophic numbers are often used by the decision makers to evaluate their opinion in multi-attribute group decision making problems. Based on the combination of triangular neutrosophic numbers and hesitant neutrosophic sets, in this paper, we propose hesitant triangular neutrosophic numbers. Also, we discuss various types of operations between them including some properties. Then, we propose various types of hesitant triangular neutrosophic weighted aggregation operators to aggregate the hesitant triangular neutrosophic information. Furthermore, we introduce score of hesitant triangular neutrosophic numbers to ranking the hesitant triangular neutrosophic numbers. Based on the hesitant triangular neutrosophic weighted aggregation operators and score of hesitant triangular neutrosophic numbers, we develop a multi attribute decision making (MADM) approach, in which the evaluation values of alternatives on the attribute are represented in terms of hesitant triangular neutrosophic numbers and the alternatives are ranked according to the values of the score of hesitant triangular neutrosophic numbers to select the most desirable one. Finally, we give a practical example, including a comparision study with the other existing method, for enterprise resource planning system selection to verify the application and effectiveness of the proposed method.

Aggregating data is the main line of any discipline dealing with fusion of information from the knowledge-based systems to decision-making. The purpose of aggregation methods is to convert a list of objects, all belonging to a given set, into a single representative object of the same set usually by an n-ary function, so-called aggregation operator. As the useful aggregation functions for modeling real-life problems are limited, the basic problem is to construct a proper aggregation operator, usually a symmetric one, for each situation. During the last decades, a number of construction methods for aggregation functions have been developed to build new classes based on the existing well-known operators. There are three main construction methods in common use: transformation, composition, and convex combination. This paper compares these methods with respect to the type of aggregating problems that can be handled by each of them.

The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that best represent the handball player's performance in a match and for setting their relevance weights. Our approach consists of a fuzzy model to aggregate expert judgments. This methodology overcomes some drawbacks of classical systems, including the definition of the relevance of each criteria using linguistic labels. A preliminary evaluation analyzes handball players' performance indicators and their application to a short tournament. Considering the obtained results, we can conclude that the proposal is relevant and provides useful insights regarding player performance in different matches. The proposed methodology has also been compared with a basic plus-minus rating methodology. This comparison illustrates the feasibility of our approach. Results suggest that plus-minus rating is not the best solution to represent the performance of specialized players who only play when their team attack or defense. Our approach demonstrates being more appropriate for sports such as handball because it includes the valuation of a full set of positive actions in defense and attack.

Neutrosophic cubic fuzzy sets (NCFSs) involve interval valued and single valued neutrosophic sets, and are used to describe uncertainty or fuzziness in a more efficient way. Aggregation of neutrosopic cubic fuzzy information is crucial and necessary in a decision making theory. In order to get a better solution to decision making problems under neutrosophic cubic fuzzy environment, this paper introduces an aggregating operator to neutrosophic cubic fuzzy sets with the help of Bonferroni mean and geometric mean, and proposes neutrosophic cubic fuzzy geometric Bonferroni mean operator (NCF GBMu,v) with its properties. Then, an efficient decision making technique is introduced based on weighted operator WNCF GBMu,v w . An application of the established method is also examined for a real life problem.

The principal purpose of this paper is to analyze different methods for decision making, with a focus on entrepreneurship in Latin America. Decision-making methods may be informed by aggregation operators that are based on the use of probabilities, weighted averages (WAs) and generalized aggregation operators. The paper presents a new generalized probabilistic weighted averaging (GPWA) operator that unifies WAs and probability in the same formulation, considering the degree of importance of each concept used in the analysis. The fundamental advantage of this approach is that it includes a wide range of particular cases including the probabilistic weighted averaging (PWA) operator, the probabilistic weighted geometric averaging (PWGA) operator and the probabilistic weighted quadratic averaging (PWQA) operator. Quasi-arithmetic means are used to obtain the Quasi-PWA operator and to generalize the approach, which is then applied to a set of hypothetical entrepreneurial investment decisions in a politically unified Latin American region. El objetivo principal de este artículo es analizar diferentes métodos de toma de decisiones, con enfoque en el emprendimiento en Latinoamérica. Los métodos de toma de decisiones pueden recibir información por parte de operadores de agregación basados en el uso de probabilidades, promedios ponderados (PP) y operadores de agregación generalizados. El artículo presenta un nuevo operador probabilístico generalizado de promedios ponderados (GPWA) que unifica los promedios ponderados y la probabilidad en la misma formulación, considerando el grado de importancia de cada concepto usado en el análisis. La ventaja fundamental de este enfoque es que incluye un amplio rango de casos particulares, incluyendo el operador probabilístico de promedios ponderados, el operador probabilístico de promedios geométricos ponderados y el operador probabilístico de promedios cuadráticos ponderados. Se emplean medios cuasiaritméticos para obtener el operador cuasiprobabilístico de promedios ponderados y para generalizar el enfoque, que luego es aplicado a un conjunto de decisiones empresariales hipotéticas en cuanto a inversión en una región latinoamericana unificada políticamente. L'objectif principal de cet article consiste à analyser diverses méthodes pour prendre des décisions du point de vue de l'entrepreneu-riat en Amérique latine. Ces méthodes peuvent recevoir une information d'opérateurs d'agrégation basés sur l'utilisation de probabilités, moyennes pondérées (Mp) et opérateurs d'agrégation généralisés. L'article présente un nouvel opérateur de probabilités généralisé de moyennes pondérées (GPWA) qui unifie les moyennes pondérées et la probabilité dans une même formulation, considérant le degré d'importance de chacun des concepts utilisés dans l'analyse. L'avantage fondamental de cette approche consiste en ce qu'elle inclut un vaste éventail de cas particuliers: l'opérateur probabiliste de moyennes pondérées, le probabiliste de moyennes géométriques pondérées et le probabiliste de moyennes quadratiques pondérées. Des moyens quasi-arithmétiques sont utilisés pour obtenir l'opérateur quasi-probabiliste de moyennes pondérées et pour gé-néraliser l'approche qui, ensuite, est appliquée à un ensemble de décisions entrepreneuriales hypothétiques à propos de l'investissement dans une région latino-américaine unifiée politiquement. O objetivo principal deste artigo é o de analisar diferentes métodos para tomar decisões, com enfoque no empreendedorismo na América Latina. Esses métodos podem receber informação de operadores de agregação apoiados no uso de probabilidades, médias ponderadas (MP) e operadores de agregação generalizados. O artigo apresenta um novo operador probabilístico generalizado de médias ponderadas (GMWA) que unifica as médias ponderadas e a probabilidade na mesma formulação, considerando o grau de importância de cada conceito utilizado na análise. A vantagem fundamental desta visão é que inclui uma ampla faixa de casos particulares: O operador probabilístico de médias ponderadas, o probabilístico de médias geométricas ponderadas e o probabilístico de médias quadráticas ponderadas. São utilizadas médias quasearitméticas para obter o operador quaseprobabilístico de médias ponderadas e para generalizar a visão e, depois é aplicado a um conjunto de decisões empresariais hipotéticas quanto a investimento em uma região latino-americana unificada politicamente.

From last few decades criminal activities and terror attacks have been increased. In a big city or in highly populated city, it is very difficult to keep under surveillance each and every area of that city throughout whole day and night. Since, it will require huge amount of deployment of police duty and such type of deployment can cause heavy work load on the enforcement agencies. It has been seen that, some types of areas are often targeted by terrorist than others, mainly train junctions, bus stoppages, market places, near temples etc. In this paper, a new aggregation operator on generalized triangular fuzzy number has been defined. Through the newly developed aggregation operator, a fuzzy multi criteria decision making approach has been constructed which enables us to determine the area of a city which may be the potentially under threat, during any interval of time on a regular working day.

The objective of this paper is to present new interactive averaging aggregation operators by assigning associate probabilities for T-spherical fuzzy sets (T-SFSs). T-SFS is a generalization of several existing theories such as intuitionistic fuzzy sets and picture fuzzy sets to handle imprecise information. Under such an environment, we developed a series of averaging interactive aggregation operators under the features that each element is represented with T-spherical fuzzy numbers. Various properties of the proposed operators are also investigated. Further, to rank the different T-SFSs, we exhibit the new score functions and state their some properties. To demonstrate the presented algorithm, a decision-making process algorithm is presented with T-SFS features. To save non-renewable resources and to the protect environment, the use of renewable resources is important. Solar energy is one of the best renewable energy resources and is also environment-friendly and thus the selection of solar cells is typically a multi-attribute decision-making problem. Therefore, the applicability of the developed algorithm is demonstrated with a numerical example in the selection of the solar cells and comparison of their performance with the several existing approaches.

Interval-valued Pythagorean fuzzy (IVPF) set is one the successful extension of the existing theories for handling the uncertainties during the decision-making process. Under that environment, various aggregation operators have been developed by the authors to aggregate the different preferences of the decision makers under the different attributes. But these studies have conducted under the assumption that their corresponding pairs are independent and don’t consider the interaction between the pairs of the membership degrees. In this paper, these conditions have been relaxed by considering the interrelationship between the different inputs by using Maclaurin symmetric mean (MSM) operator. Further, based on the input and MSM operator, we proposed two aggregation operators namely, IVPF Maclaurin symmetric mean and IVPF weighted Maclaurin symmetric mean operators and studied their desirable properties. A decision-making method based on these operators has been discussed for solving the decision-making problems under IVPF set environment. Finally, an illustrative example and a comparative analysis have been presented to demonstrate the proposed approach.

Given the consequences of the recent financial crisis, there is an increased interest in modelling and predicting the behaviour of complex financial systems. As a novel approach to measuring risk in networks, this paper proposes RiskRank as a general-purpose aggregation operator of risk in nodes and links. RiskRank relies on a system represented as a hierarchical network, where node values and linkages represent individual risk levels and interconnectedness, respectively. The measure is used to aggregate risk in the vein of a novel network centrality measure, allowing for the integration of the interrelations of different entities in the network with any other measure of node risk. The use of RiskRank is illustrated through a real-world case on systemic risk in Europe, in which we show that it improves performance in out-of-sample analysis. We provide an estimation of systemic risk from country-level risk indicators and combine it with cross-border linkages to illustrate the practical benefit of the proposed approach. From a policy perspective, our results strengthen the results of previous research and underline the importance of integrating a network perspective in macro-prudential analysis.

The ordered weighted averaging (OWA) operator is one of the most used techniques in the operator’s aggregation procedure. This paper proposes a new assignment algorithm by using the OWA operator and different extensions of it in the Branch-and-bound algorithm. The process is based on the use of the ordered weighted average distance operator (OWAD) and the induced OWAD operator (IOWAD). We present it as the Branch-and-bound algorithm with the OWAD operator (BBAOWAD) and the Branch-and-bound algorithm with the IOWAD operator (BBAIOWAD). The main advantage of this approach is that we can obtain more detailed information by obtaining a parameterized family of aggregation operators. The application of the new algorithm is developed in a consumer decision-making model in the city of Barcelona regarding the selection of groceries by districts that best suit their needs. We rely on the opinion of local commerce experts in the city. The key advantage of this approach is that we can consider different sources of information independent of each other.

The main aim of this paper is to establish that essential aspects are determinant in the enterprise risk identification (ERI) and the existing interrelationship between each corporate risk goals, in the large companies of Colombia. Study proposes a parametric analysis and a non-parametric. The first uses correlation matrices for statistical analysis, a multiple linear regression statistical tools to identify that essential aspects are determinant in the (ERI). The second proposes a new aggregation called the Bonferroni Induced Ordered Weighted Average Adequacy Coefficient (BON-IHOWAAC) operator and Bonferroni Induced Ordered Weighted Average the Maximum and Minimum level (BON-IHOWAIMAM) to establish the existing interrelationship between each corporate risk goals using the risk management information and manager perception. Of the results obtained is highlighted that for all economic sectors; first, control measures are highlighted in the (ERI) and second, the goal with the greatest interrelationship for the other ones to be achieved is protect people. Finally, the study concludes with a holistic analysis of the importance that executive team gives to the management of risks from the prioritization of objectives and the use of tools for the treatment of information to improve the process of decisions-making in uncertain contexts. First published online 5 April 2019

In this paper, the existing definition of the group-based generalized intuitionistic fuzzy soft set is clarified and redefined by merging intuitionistic fuzzy soft set over the set of alternatives and a group of intuitionistic fuzzy sets on parameters. In this prospect, two new subsets of the group-based generalized intuitionistic fuzzy soft set are proposed and several operations are contemplated. The two new aggregation operators called generalized group-based weighted averaging and generalized group-based weighted geometric operator are introduced. The related properties of proposed operators are discussed. The recent research is emerging on multi-attribute decision making methods based on soft sets, intuitionistic fuzzy soft sets, and generalized intuitionistic fuzzy soft sets. An algorithm is structured and two case studies of multi-attribute decision makings are considered using proposed operators. Further, we provide the comparison and advantages of the proposed method, which give superiorities over recent major existing methods.

Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility.

Group decision making has been widely studied since group decision making processes are very common in many fields. Formal representation of the experts’ opinions, aggregation of assessments or selection of the best alternatives have been some of main areas addressed by scientists and researchers. In this paper, we focus on another promising area, the study of group decision making processes from the concept of influence and social networks. In order to do so, we present a novel model that gathers the experts’ initial opinions and provides a framework to represent the influence of a given expert over the other(s). With this proposal it is feasible to estimate both the evolution of the group decision making process and the final solution before carrying out the group discussion process and consequently foreseeing possible actions.

In fuzzy set theory, t-norms and t-conorms are fundamental binary operators. Yager proposed respective parametric families of both t-norms and t-conorms. In this paper, we apply these operators for the analysis of Pythagorean fuzzy sets. For this purpose, we introduce six families of aggregation operators named Pythagorean fuzzy Yager weighted averaging aggregation, Pythagorean fuzzy Yager ordered weighted averaging aggregation, Pythagorean fuzzy Yager hybrid weighted averaging aggregation, Pythagorean fuzzy Yager weighted geometric aggregation, Pythagorean fuzzy Yager ordered weighted geometric aggregation and Pythagorean fuzzy Yager hybrid weighted geometric aggregation. These tools inherit the operational advantages of the Yager parametric families. They enable us to study two multi-attribute decision-making problems. Ultimately we can choose the best option by comparison of the aggregate outputs through score values. We show this procedure with two practical fully developed examples.

Hesitant fuzzy sets (HFSs), which were generalized from fuzzy sets, constrain the membership degree of an element to be a set of possible values between zero and one, furthermore, if two or more decision-makers select the same value, it is only counted once. However, a situation where the evaluation value is repeated several times differs from one where the value appears only once. Multi-hesitant fuzzy sets (MHFSs) can deal effectively with a case where some values are repeated more than once in a MHFS. In this paper, the novel convex combination of multi-hesitant fuzzy numbers (MHFNs) is introduced. Some aggregation operators based on convex operation, such as generalized multi-hesitant fuzzy ordered weighted average (GMHFOWA) operator, generalized multi-hesitant fuzzy hybrid weighted average (GMHFHWA) operator, generalized multi-hesitant fuzzy prioritized weighted average (GMHFPWA) operator and generalized multi-hesitant fuzzy Choquet integral weighted average (GMHFCIWA) operator, are developed and corresponding properties are discussed in detail. Then, based on the proposed aggregation operators, a novel approach for multi-criteria decision-making (MCDM) problem is proposed for ranking alternatives. Finally, an example is provided to verify the developed approach and demonstrate its validity and feasibility and the study is supported by a sensitivity analysis and a comparison analysis.

This paper proposes a model for faculty evaluation based on OWA aggregation operators. Our method permits to consider interactions among the criteria in a formal way, and, at the same time, to realize an easy approach to understand, implement and apply.

In distributed processing systems it is often necessary to coordinate the allocation of shared resources that should be assigned in the processes in the modality of mutual exclusion; in such cases, the order in which the shared resources will be assigned in the processes that require them must be decided; in this paper we propose an aggregation operator (which could be used by a shared resources manager module) that will decide the order of allocation of the resources to the processes considering the requirements of the processes (shared resources) and the state of the distributed nodes where the processes operate (their computational load). Fil: la Red Martínez, David Luis. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina Fil: Acosta, Julio César. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina Fil: Agostini, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina

Conferencia de "Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2017 and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017; Warsaw; Poland; 11 September 2017 through 15 September 2017" Group Decision Making methods are interesting tools that can be used for extracting subjective information from experts that are familiar with an specific topic. Although they are typically used for carrying out an alternatives ranking, a novel approach that can extract information from the experts and store it in a fuzzy ontology is presented in this paper. Thanks to this, it is possible to create a knowledge database using subjective information extracted from experts in the dealt topic. This database can be helpful for other users that are interested in the stored data. The obtained information is stored in a fuzzy ontology due to its capacity of using queries to access the information and because they are able to handle the information in a organized way.

Neutrosophic theory has many applications in graph theory, single valued neutrosophic graph (SVNG) is the generalization of fuzzy graph and intuitionistic fuzzy graph. In this paper, we introduced some types of SVNGs, which are subdivision SVNGs, middle SVNGs, total SVNGs and single valued neutrosophic line graphs (SVNLGs), also discussed the isomorphism, co weak isomorphism and weak isomorphism properties of subdivision SVNGs, middle SVNGs, total SVNGs and SVNLGs. Publisher's Version

Fil: La Red Martínez, David Luis. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. In distributed processing systems it is often necessary to coordinate the allocation of shared resources that should be assigned in the processes in the modality of mutual exclusion; in such cases, the order in which the shared resources will be assigned in the processes that require them must be decided; in this paper we propose an aggregation operator (which could be used by a shared resources manager module) that will decide the order of allocation of the resources to the processes considering the requirements of the processes (shared resources) and the state of the distributed nodes where the processes operate (their computational load).