Hybrid Aggregation Operators Research Articles

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

Assessment of environment-conscious propulsion technologies for road freight distribution based on T-spherical fuzzy Schweizer-Sklar power operators

This paper explores the viability of environmentally conscious propulsion technologies for road freight distribution, specifically using T-spherical fuzzy information. With the increasing need for sustainable transportation, the study investigates the potential of various propulsion technologies, including electric, hybrid, solar, and diesel vehicles, and evaluates their environmental impact in terms of emissions reduction and energy efficiency. The T-spherical fuzzy information is utilized to analyze and compare the performance of these technologies in different scenarios. In this paper, we proposed hybrid aggregation operators (AOs) namely T-spherical fuzzy Schweizer-Sklar power AOs for the aggregation of T-spherical fuzzy information. Moreover, the T-spherical fuzzy aggregation method provides a comprehensive and effective tool for evaluating the viability of different propulsion technologies. This article exhibits some characteristics of the proposed AOs. Using suggested AOs with numerous evaluations by decision-makers and partial weight information under T-spherical fuzzy information, a method for multi-criteria decision-making is constructed. The results show that the adoption of environmentally conscious propulsion technologies can significantly reduce carbon emissions and improve energy efficiency in road freight distribution. In addition, the research includes a sensitivity analysis and a comparison between the proposed strategy and current methods.

Confidence Picture fuzzy hybrid aggregation operators and its application in multi criteria group decision making

Confidence Picture fuzzy hybrid aggregation operators and its application in multi criteria group decision making

Optimizing Material Selection with Fermatean Fuzzy Hybrid Aggregation Operators

In the pursuance of engineering excellence and sustainable practices, the optimization of material selection processes plays a crucial role. Using Fermatean fuzzy aggregation Operators (AOs), this study introduces an innovative method for improving material selection procedures. Combining the advantages of Fermatean fuzzy set (FrFS) and AOs, the proposed method enables a comprehensive evaluation of materials based on multiple criteria. The authors propose two operators: the “Fermatean fuzzy hybrid weighted arithmetic geometric aggregation (FrFHWAGA) operator” and the “Fermatean fuzzy hybrid ordered weighted arithmetic geometric aggregation (FrFHOWAGA) operator”. This method facilitates informed decision making in a number of industries by taking into account factors such as cost, durability, environmental impact, and availability. This research enables engineers, designers, and decision makers to optimize material selection, resulting in more efficient, cost-effective, and sustainable solutions across multiple domains.

A novel Aczel-Alsina triangular norm-based group decision-making approach under dual hesitant q-rung orthopair fuzzy context for parcel lockers’ location selection

For complex last mile problems, the parcel lockers play an important role in urban areas. So, selecting a suitable location is very much crucial to provide optimal service and better logistical performance. From that viewpoint, a multicriteria group decision making method is developed in this article using dual hesitant q-rung orthopair fuzzy (DHq-ROF) set which is more effective than existing variations of fuzzy sets. The Aczel-Alsina (AA) class of t-conorms and t-norms (AAt-CNs&t-Ns) emerged as an important class of union and intersection operations due to its greater flexibility in the information fusion process. To take advantage of the benefits of AAt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF environments, several fundamental operations based on AAt-CNs&t-Ns are first defined. Following the introduction of these stated operations, a series of aggregation operators, viz., DHq-ROF AA weighted averaging, ordered weighted averaging, hybrid averaging and their geometric versions with DHq-ROF information, has been proposed. Based on these operators, a creative approach to handle multi-attribute group decision-making problems has been framed. The parcel lockers’ location selection problem is estimated to validate the created strategy and show its applicability and efficacy. The achieved results establish that the post office is the best location for locating parcel lockers.

An improved Pythagorean fuzzy hybrid aggregation method based on grey correlation for knowledge collaborative innovation partner selection

In the context of open innovation, selecting partners for knowledge collaboration is crucial for knowledge-intensive enterprises, and matching cooperation is key to successful intellectual property cooperation. To provide enterprises with practical tools for partner selection, this paper analyzes the evaluation factors of intellectual property partners. We establish a collaborative innovation intellectual property partner selection model by combining the maximum entropy model with grey relational method, and calculating the comprehensive evaluation value of candidate enterprises by using the improved Pythagorean Fuzzy Hybrid Aggregation (PF-HA) operator. An application example illustrates the feasibility and advantage of the improved PF-HA method improving the selection of intellectual property partners. Compared with other methods, the advantages of PF-HA are shown in that it can simultaneously optimize the use efficiency of multi-partner and multi-dimensional evaluation data, and effectively deal with the ambiguity of expert decision information and the flexibility of index weight in the partner evaluation process.

Schweizer‐Sklar operations based hybrid aggregation operator to dual hesitant q‐rung orthopair fuzzy set and its application on MCGDM

AbstractDual hesitant q‐rung orthopair fuzzy (DHq‐ROF) set appears as a powerful tool in compare to other variants of fuzzy sets to deal with uncertainties associated with available information in various real‐life decision‐making cases. In order to make DHq‐ROF aggregation information process flexible, at first some operations viz., addition, multiplication, scalar multiplication, exponential laws based on Schweizer‐Sklar class of t‐conorms and t‐norms are defined. Subsequently, using these operations, weighted average and geometric operators and ordered weighted average and geometric operators are introduced. But weighted average or geometric operators and ordered weighted average or geometric operators consider only the weight of the opinions and the weight of the ordered position of each given opinion respectively. To resolve weights of the arguments, hybrid aggregation operators viz., DHq‐ROF Schweizer‐Sklar hybrid averaging, DHq‐ROF Schweizer‐Sklar hybrid geometric operators are developed and their properties are discussed. Afterwards, a new method to deal with multicriteria group decision making problems under DHq‐ROF environment is framed. To illustrate the proposed method a decision making problem related to investment company selection is considered and solved. To show the advantages of the proposed study, a comparative analysis among the developed and existing studies is discussed.

Multi-criteria group decision-making algorithm based on single-valued neutrosophic Einstein prioritized aggregation operators and its applications

PurposeSingle-valued neutrosophic sets (SVNSs) are efficient models to address the complexity issues potentially with three components, namely indeterminacy, truthness and falsity. Taking advantage of SVNSs, this paper introduces some new aggregation operators (AOs) for information fusion of single-valued neutrosophic numbers (SVNNs) to meet multi-criteria group decision-making (MCGDM) challenges.Design/methodology/approachEinstein operators are well-known AOs for smooth approximation, and prioritized operators are suitable to take advantage of prioritized relationships among multiple criteria. Motivated by the features of these operators, new hybrid aggregation operators are proposed named as “single-valued neutrosophic Einstein prioritized weighted average (SVNEPWA) operator” and “single-valued neutrosophic Einstein prioritized weighted geometric (SVNEPWG) operators.” These hybrid aggregation operators are more efficient and reliable for information aggregation.FindingsA robust approach for MCGDM problems is developed to take advantage of newly developed hybrid operators. The effectiveness of the proposed MCGDM method is demonstrated by numerical examples. Moreover, a comparative analysis and authenticity analysis of the suggested MCGDM approach with existing approaches are offered to examine the practicality, validity and superiority of the proposed operators.Originality/valueThe study reveals that by choosing a suitable AO as per the choice of the expert, it will provide a wide range of compromise solutions for the decision-maker.

Cubic M-polar Fuzzy Hybrid Aggregation Operators with Dombi’s T-norm and T-conorm with Application

A cubic m-polar fuzzy set (CmPFS) is a new hybrid extension of cubic set (CS) and m-polar fuzzy set (mPFS). A CS comprises two parts; one part consists of a fuzzy interval (may sometimes be a fuzzy number) acting as membership grade (MG), and the second part consists of a fuzzy number acting as non-membership grade (NMG). An mPFS assigns m number of MGs against each alternative in the universe of discourse. A CmPFS deals with single as well as multi-polar information in the cubic environment. In this article, we explore some new aspects and consequences of the CmPFS. We define score and accuracy functions to find the priorities of alternatives/objects in multi-criteria decision-making (MCDM). For this objective, some new operations, like addition, scalar/usual multiplication, and power, are defined under Dombi’s t-norm and t-conorm. We develop several new aggregation operators (AOs) using cubic m-polar fuzzy Dombi’s t-norm and t-conorm. We present certain properties of suggested operators like monotonicity, commutativity, idempotency, and boundedness. Additionally, to discuss the application of these AOs, we present an advanced superiority and inferiority ranking (SIR) technique to deal with the problem of conversion from a linear economy to a circular economy. Moreover, a comparison analysis of proposed methodology with some other existing methods is also given.

Generalized intuitionistic fuzzy aggregation operators based on confidence levels for group decision making

The focus of our this paper is to develop a series of Einstein hybrid aggregation operators using confidence level, such as confidence intuitionistic fuzzy Einstein hybrid averaging operator, confidence intuitionistic fuzzy Einstein hybrid geometric operator, generalized confidence intuitionistic fuzzy Einstein hybrid averaging operator and generalized confidence intuitionistic fuzzy Einstein hybrid geometric operator. The main advantage of the new operators is that these operators not only provide information to the experts of the problems, but these methods also consider the degrees of the experts of the problems that they are familiar with the selection option. The new approaches provide more general, more accurate and precise results as compared to the existing methods. Finally, the proposed operators have been applied to decision-making problems to show the validity, practicality and effectiveness of the new approach.

Some q-rung orthopair fuzzy hybrid aggregation operators and TOPSIS method for multi-attribute decision-making

Some q-rung orthopair fuzzy hybrid aggregation operators and TOPSIS method for multi-attribute decision-making

Some Aggregation Operators For Bipolar-Valued Hesitant Fuzzy Information

In this article we define some aggregation operators for bipolar-valued hesitant fuzzy sets. These operations include bipolar-valued hesitant fuzzy ordered weighted averaging (BPVHFOWA) operator, bipolar-valued hesitant fuzzy ordered weighted geometric (BPVHFOWG) operator and their generalized forms. We also define hybrid aggregation operators and their generalized forms and solved a decision-making problem on these operations.

T-Spherical Fuzzy Einstein Hybrid Aggregation Operators and Their Applications in Multi-Attribute Decision Making Problems

T-spherical fuzzy set is a recently developed model that copes with imprecise and uncertain events of real-life with the help of four functions having no restrictions. This article’s aim is to define some improved algebraic operations for T-SFSs known as Einstein sum, Einstein product and Einstein scalar multiplication based on Einstein t-norms and t-conorms. Then some geometric and averaging aggregation operators have been established based on defined Einstein operations. The validity of the defined aggregation operators has been investigated thoroughly. The multi-attribute decision-making method is described in the environment of T-SFSs and is supported by a comprehensive numerical example using the proposed Einstein aggregation tools. As consequences of the defined aggregation operators, the same concept of Einstein aggregation operators has been proposed for q-rung orthopair fuzzy sets, spherical fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and intuitionistic fuzzy sets. To signify the importance of proposed operators, a comparative analysis of proposed and existing studies is developed, and the results are analyzed numerically. The advantages of the proposed study are demonstrated numerically over the existing literature with the help of examples.

N-Intuitionistic Polygonal Fuzzy Aggregation Operators and Their Application to Multi-Attribute Decision Making

The n-intuitionistic polygonal fuzzy set (n-IPFS), combined by the intuitionistic fuzzy and polygonal fuzzy sets, is an extended form of the triangular intuitionistic fuzzy set (TIFS) and the trapezoidal intuitionistic fuzzy set (TrIFS). The aim of this paper is to develop some new aggregation operators for n-IPFSs and apply them to multi-attribute group decision making (MAGDM) problems. First, the operational properties and the score function of n-IPFSs are defined. Then, three kinds of n-intuitionistic polygonal fuzzy aggregation operators are investigated including n-intuitionistic polygonal fuzzy weighted averaging (n-IPFWA) operator, n-intuitionistic polygonal fuzzy ordered weighted averaging (n-IPFOWA) operator and n-intuitionistic polygonal fuzzy hybrid aggregation (n-IPFHA) operator. Finally, we propose an improved technique for order preference by similarity to an ideal solution (TOPSIS) approach with n-IPFSs and unknown attributes weights. The attributes weights are obtained by combining the entropy weights and the subjective weights, and the entropy weights are calculated based on the score function of n-IPFS. The spatial closeness reflected by the Hamming distance and the grey relationship with the positive/negative solution are both considered in getting the relative closeness degree to rank the alternatives. The example analysis of a location selection is given to verify the practicality and the effectiveness of the proposed approach in this paper.

Multiple Attribute Group Decision Making Methods Using Numerical Methods of Intuitionistic Triangular Fuzzy Sets

Solving Multiple Attribute Group Decision Making (MAGDM) problems has become one of the most important research in the recent trends. The information or data is in the form of Intuitionistic Triangular Fuzzy Number (ITrFN). The weights are derived from the numerical solutions of partial differential equations of Laplace’s equation and poisson`s equation. The weights obtained from the methods are applied in decision making problems. The Intuitionistic Triangular Fuzzy Ordered Weighted Averaging (ITrFOWA) operator and the Intuitionistic Triangular Fuzzy Hybrid Aggregation (ITrFHA) operator are used to combine the decision matrix. Distance function is used as a tool for ranking the best alternatives. Numerical illustration is proposed to show the effectiveness of the method.

Some Generalised Einstein Hybrid Aggregation Operators and Their Application to Group Decision-making Using Pythagorean Fuzzy Numbers

Pythagorean fuzzy set (PFS) is one of the prosperous extensions of the intuitionistic fuzzy set (IFS) for handling the fuzziness and uncertainties in the data. Under this environment, in this paper, we introduce the notion of two generalised Einstein hybrid operators namely, generalised Pythagorean fuzzy Einstein hybrid averaging (in short GPFEHA) operator and generalised Pythagorean fuzzy Einstein hybrid geometric (in short GPFEHG) operator along with their desirable properties, such as idempotency, boundedness and monotonicity. The main benefit of the proposed operators is that these operators deliver more general, more correct and precise results as compared to their existing methods. Generalised Einstein operators combine Einstein operators with some generalised operators using Pythagorean fuzzy values. Therefore these methods play a vital role in real world problems. Finally, the proposed operators have been applied to decision-making problems to show the validity, practicality and effectiveness of the new attitude.

Intuitionistic Trapezoidal Fuzzy MAGDM Problems with Sumudu Transform in Numerical Methods

Multiple attribute group decision making (MAGDM) is an important scientific, social, and economic endeavor. The ability to make consistent and correct choices is the essence of any decision process imbued with uncertainty. In situations where the information or data is in the form of an intuitionistic trapezoidal fuzzy numbers, or to construct the MAGDM problem, an intuitionistic trapezoidal fuzzy weighted averaging (ITzFWA) operator and an intuitionistic trapezoidal fuzzy hybrid aggregation (ITzFHA) operator are used. In this article, the decision maker provides the weights for aggregation in the form of an initial value problem of ordinary differential equations based on the study made on the data given by the decision maker. Decision maker weights are derived through sumudu transformation and various other numerical methods by obtaining the solution of differential equations. A numerical illustration is given to show the effectiveness and feasibility of the proposed approach.

Logarithmic Hybrid Aggregation Operators Based on Single Valued Neutrosophic Sets and Their Applications in Decision Support Systems

Logarithmic Hybrid Aggregation Operators Based on Single Valued Neutrosophic Sets and Their Applications in Decision Support Systems

Group decision making method based on hybrid aggregation operator for intuitionistic uncertain linguistic variables

Group decision making method based on hybrid aggregation operator for intuitionistic uncertain linguistic variables

A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set

As a generalization to hesitant fuzzy linguistic term set (HFLTS), double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is presented which circumvents the weakness of HFLTS in representing complex linguistic terms. DHHFLTS has two linguistic hierarchies with the second hierarchy supplementing the primary which enables decision-makers (DMs) to represent complex linguistic terms better. Motivated by the power of DHHFLTS, in this paper, a new decision framework is presented under DHHFLTS context. Initially, a new aggregation operator called double hierarchy hesitant fuzzy hybrid aggregation (DHHFHA) operator is proposed for sensible aggregation of DMs’ preference information. Further, weights of attributes are calculated by extending statistical variance (SV) method under DHHFLTS context. Objects are prioritized by extending the popular WASPAS (weighted aggregated sum product assessment) method to DHHFLTS context. The applicability and usefulness of the proposed framework are realized by demonstrating a risk management technique (RMT) selection problem for a construction project. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.