Geometric Averaging Operator Research Articles

Bipolar Complex Fuzzy Rough Sets and Their Applications in Multicriteria Decision Making

Bipolar complex fuzzy set (BCFS) is a more advanced and powerful phenomenon as it consists of two-dimensional data with positive and negative impacts of an element. It can solve the data consisting of the positive and negative impacts of an element which is a bipolar fuzzy set (BFS). It also covers the two-dimensional complex data which is a complex fuzzy set (CFS). Due to these attributes, BFS and CFS are less useful in comparison with BCFS to capture vagueness, complexity, and ambiguity in the data. Furthermore, lower and upper approximations based on equivalency relations constitute another significant phenomenon known as rough set (RS). This structure is also more powerful in dealing with real-life dilemmas. Rather than comparing the RS and BCFS, we combine both phenomena to handle the complexity more powerfully to deal with such types of phenomena that are not handled by other structures. So, by combining both phenomena, we introduce a novel structure known to be bipolar complex fuzzy rough set (BCFRS) in this manuscript. After that, we define some important operations, some significant properties related to this structure, and some aggregation operators (AOs) to solve decision-making (DM) problems related to cyber security. We address a practical application of cyber security (C-S) in computing for the protection of critical data to demonstrate the usefulness of the multi-attribute DM(MADM) approach. Based on the various criteria and attributes given by the experts, we find the best and better alternative to the C-S by applying the MADM approach. We get the A4 as the best and finest alternative by using bipolar complex fuzzy rough (BCFR) weighted arithmetic averaging (BCFRWAA), BCFR ordered weighted arithmetic averaging (BCFROWAA), and BCFR ordered weighted geometric averaging (BCFROWGA) operators. And, by using BCFR weighted geometric averaging (BCFRWGA), we get the A3 as the finest alternative. Lastly, to prove the superiority, validity, and generalization of our unique established theory, we give a detailed comparative study of our established work with several prevalent theories.

A Novel Hesitant Cubical Dombi Fuzzy Aggregation Operators for Selecting Green Supplier Chain Managements

A hesitant fuzzy (HF) set enhances the concept of fuzzy sets by addressing disagreements among decision-makers about the membership degree of an element. Similarly, the Cubical Fuzzy Set (CFS) is useful for managing uncertainty in decision-making problems. However, existing methods often lack integration of hesitation and cubical uncertainty, and there is limited exploration of their combined effects on aggregation processes. In this paper, we introduce the Hesitant Cubical Fuzzy Set (HCFS), which integrates the principles of HF sets and CFS to address these limitations. We define several set-theoretical operations for HCFSs and develop Dombi operations for them. Furthermore, we present a range of aggregation operators based on Dombi operations, including the Hesitant Cubical Dombi Fuzzy Weighted Arithmetic Averaging (HCDFWAA) Operator, the Hesitant Cubical Dombi Fuzzy Weighted Geometric Averaging (HCDFWGA) Operator, the Hesitant Cubical Dombi Fuzzy Ordered Weighted Arithmetic Averaging (HCDFOWAA) Operator, and the Hesitant Cubical Dombi Fuzzy Ordered Weighted Geometric Averaging (HCDFOWGA) Operator, and examine their properties. Additionally, we propose a multi-criteria group decision-making method and algorithm within the Hesitant Cubical Fuzzy framework. To address gaps in practical application, we provide an example of the selection of green suppliers in supply chain management. We also perform a comparative analysis with existing operators to highlight the advantages and effectiveness of our approach, emphasizing how the integration of hesitation and cubical uncertainty can enhance decision-making processes.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Determination of the most influential robot in the medical field by utilizing the bipolar complex fuzzy soft aggregation operators

From science fiction to the hospital, robots are transforming the medical field. These robots mostly look like R2D2 from Star Wars more than humanoids, yet they are having a substantial impact on the medical field. Robots in the medical field support by releasing medical staff from repetitive chores that distract their consideration to more vital responsibilities and by dropping the danger and expenditure of medical treatment for patients. The robots can also move hazardous materials and execute accurate surgery in small spaces. Considering this influence of robots on the medical field, in this manuscript, we investigate the influence of robots on the medical field and discover which robot is more influential in the medical field. For this, the bipolar complex fuzzy soft set (BCFSS) is a substantial mathematical model that covers genuine decision-making (DM) dilemmas where the obscurity and imprecision contain a negative pole, 2nd dimension, and parameterization at one time. Moreover, in this study, we establish arithmetic and geometric aggregation operators (AOs) in the setting of BCFSS, like bipolar complex fuzzy soft-weighted arithmetic averaging (BCFSWAA) and bipolar complex fuzzy soft-weighted geometric averaging (BCFSWGA) operators. Moreover, for discovering the most influential robot, we invent a technique of DM relying on the diagnosed AOs in the environment of BCFSS, and then, we describe a numerical example (determination of the most influential robot in the medical field). To demonstrate the superiority and condescension of our work we compare it with prevailing work.

Novel method for total organic carbon content prediction based on non-equigap multivariable grey model

Predicting total organic carbon content plays a crucial role in shale gas reservoir evaluation. To address multi-variable, imperfect logging data and non-equigap characteristics, this study developed a multi-variable grey derivative model for predicting the total organic carbon content trend. To begin with, the non-equigap factor is introduced and the parameter identification of the new model is established on the basis of least square method. Then, to reduce fluctuations in the original data, the weighted geometric average weakening operator is introduced. In addition, considering the system stability, the derivation method is used to calculate the time response formula of the model. Furthermore, two validation cases are provided to verify the validity of this novel model. Finally, the model is applied to the Fuling shale gas field in China for practical forecasting. Compared to other models, such as the grey model, statistical model, and artificial intelligence model, the proposed model achieved higher prediction accuracy in the three cases. Specifically, this model obtained 95.62%, 95.88% and 94.55% prediction accuracy for total organic carbon content. Results that the new model effectively addresses the limitations of previous studies that focus on single-parameter or equal spacing issues.

Target Damage Assessment Method Based on Interval‐Valued Intuitionistic Fuzzy Aggregation Operator under Dynamic Multi‐Expert

This study aims to solve the problem that the current target damage assessment method does not fully take into account the time series characteristics of the target and the dynamic changes in the battlefield situation, thus leading to insufficient accuracy of the assessment results. Target damage assessment method based on interval‐valued intuitionistic fuzzy aggregation operator (IVIFAO) under dynamic multi‐experts is a combination of interval‐valued intuitionistic fuzzy set (IVIFS) theory and dynamic multi‐attribute swarm decision‐making theory. Firstly, we extracted the effective factors that cause damage to the target and established the damage assessment index system. Secondly, in order to take into account the subjective weights and objective weights, we proposed a comprehensive weight model of damage factors based on interval‐valued intuitionistic fuzzy entropy (IVIFE) based on a cosine function (IVIFECF). The spatio‐temporal weights for each moment are determined using the Poisson distribution method. Then, given the inherent ambiguity and complexity of the decision‐making process, the decision‐making information is represented by interval‐valued intuitionistic fuzzy numbers (IVIFNs). We constructed a weighted interval‐valued intuitionistic fuzzy (WIVIF) decision matrix. Here, the interval‐valued intuitionistic fuzzy weighted geometric average (IVIFPWGA) operator is used to synthesize decision‐making information related to damage factors. We further introduce the dynamic interval‐valued intuitionistic fuzzy power‐weighted geometric average (DIVIFPWGA) operator, which combines instantaneous decision information with time series weights to generate a dynamic multi‐expert fusion WIVIF decision matrix. Finally, the improved TOPSIS method is used to rank the IVIFNs, resulting in a comprehensive damage assessment of the targets, which is analysed for superiority with the novel method, comparing both real‐time and scalability. The stability and validity of the method were verified.

A dual-level multi-attribute group decision-making model considering interaction factors based on CCSD and MARCOS methods with R-numbers

The current body of research on multi-attribute group decision-making (MAGDM) exhibits some limitations, namely the utilization of the single attribute hierarchy and the assumption of attribute independence. This paper presents a novel approach, referred to as the R-DLMAGDM (R-numbers Dual-Level Multi-Attribute Group Decision-Making) model with interaction factors, which integrates the advantages of R-numbers in risk evaluation. The proposed model aims to address the limitations related to attribute hierarchies and the assumption of attribute independence. The first step in the new model involves establishing the entropy model with R-numbers to determine the expert weights. Subsequently, the R-numbers generalized weighted arithmetic average (RNGWAA) operator and the R-numbers generalized weighted geometric average (RNGWGA) operator are introduced to combine the information provided by the experts. Next, the application of the enhanced correlation coefficient and standard deviation (CCSD) method is utilized to ascertain the relative weights of the dual-level attributes using the inverse order concept. Then, the fuzzy cognitive map (FCM) is employed to evaluate the interrelationships among components in order to derive the final attribute weights. Additionally, the paper discusses the implementation of the R-DLMAGDM model for the assessment of risk in a virtual supply chain in the metaverse. The evaluation process entails the prioritizing of five different alternatives based on four Level 1 criteria and seven Level 2 criteria. The model’s flexibility and validity are showcased through the execution of comprehensive sensitivity analysis and three-dimensional comparative analysis.

Intuitionistic uncertain linguistic prioritized aggregation operators and their application to multi-criteria decision making

The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers (IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator.

The optimal interval combination prediction model based on vectorial angle cosine and a new aggregation operator for social security level prediction

AbstractThis paper puts forwards the induced generalized ordered weighted multiple continuous ordered weighted geometric averaging (IGOWMC‐OWGA) operator, which overcomes the deficiency that the ordered weighted geometric averaging (C‐OWGA) operator can only integrate a single interval. Main properties of the proposed operator are investigated. In order to integrate interval data and obtain more accurate prediction results, an optimal model based on IGOWMC‐OWGA operator and vectorial angle cosine is constructed. Finally, the model is applied to an empirical analysis of the moderate measure value of social security level about a certain province in China. The sensitivity and comparisons of results are analyzed which shows that the established model is of good stability and can effectively improve the accuracy of prediction.

Some aggregation operators of parameterized single-valued neutrosophic numbers and their MADM approach in simplified neutrosophic indeterminate scenarios

Simplified neutrosophic indeterminate sets/numbers (SNISs/SNINs) can conveniently express the partially determinate and partially indeterminate information of truth, falsity, indeterminacy membership degrees, which shows its main merit. Then, the existing aggregation algorithms of SNINs lack a parameterized aggregation method to reach a flexible decision-making method subject to different decision risks. Therefore, this study proposed a MADM approach using some aggregation operators of parameterized single-valued neutrosophic numbers in a simplified neutrosophic indeterminate scenario. First, we presented a conversion technique from SNINs to parameterized single-valued neutrosophic numbers (PSVNNs) by an indeterminate parameter. Second, we proposed the score function of PSVNN and the parameterized single-valued neutrosophic weighted arithmetic averaging (PSVNWAA) and parameterized single-valued neutrosophic weighted geometric averaging (PSVNWGA) operators. Third, a multiple attribute decision-making (MADM) approach with the smaller, moderate and larger decision risks is developed by the PSVNWAA or PSVNWGA operator in a SNIS setting. Forth, the developed MADM approach is applied to the choice problem of clinical physicians as a numerical example. Generally, the developed MADM approach reveals the advantage of flexible decision making subject to the smaller, moderate, and larger decision risks of decision makers in a neutrosophic indeterminate scenario. However, this study reveals the main contributions in the aggregation algorithms of PSVNNs and flexible MADM approach in a SNIS setting.

Dynamic risk assessment of hybrid hydrogen-gasoline fueling stations using complex network analysis and time-series data

Since hybrid hydrogen-gasoline fueling stations store two types of hazardous chemicals, the number of victims might be substantially higher than at gas or hydrogen fueling stations during a leakage or explosion. Therefore, it is crucial to conduct an in-depth analysis and risk assessment of hybrid hydrogen-gasoline fueling stations. We establish a time series risk assessment model and use complex network analysis to analyze potential fire and explosion events in hybrid hydrogen-gasoline fueling stations. The complex network model is used to assess the structural characteristics of the complex hybrid hydrogen-gasoline fueling stations, extract the accident causal chain, and explain the relationship between the accident causal factors and the system's risk from a multi-dimensional perspective. Subsequently, time-ordered weighted averaging (TOWA) and time-ordered weighted geometric averaging (TOWGA) operators are incorporated into the complex network model. The TOWA-TOWGA hybrid operator combines the evaluation values of the summer and winter periods to obtain the dynamic risk assessment results. The static and dynamic assessment results are used to determine the degree of influence of the accident causal factors on the system risk in different periods and dimensions. The information is suitable for developing highly targeted measures to prevent/control high-risk disaster events in hybrid hydrogen-gasoline fueling stations.

Hybrid weighted arithmetic and geometric averaging operator of cubic Z-numbers and its decision-making method

A Z-number (ZN) presented by Zadeh is a new framework of fuzzy sets that combines a restriction value with a reliability measure. Based on this motivation of the concept of ZN, this study first proposes a cubic Z-number (CZN) set and operational relations of CZNs, and then defines two expected functions of CZNs to rank CZNs. Next, we present a CZN hybrid weighted arithmetic and geometric averaging (CZNHWAGA) operator and its properties. Then, a multicriteria DM model with unknown criterion weights is established based on the proposed CZNHWAGA operator, the expected functions, and the standard deviation of the expected values in the setting of CZNs. Lastly, the established DM model is applied to the selection of solar cells to illustrate its practicability and effectiveness in the setting of CZNs. However, the established DM model can solve the problem of unknown criterion weights and strengthen the DM reliability and flexibility.

Aggregation operators of quadripartitioned single-valued neutrosophic Z-numbers with applications to diverse COVID-19 scenarios

Z-number, proposed by Zadeh is an ordered pair of fuzzy numbers which have the capability to depict both the reliability and certainty of any available information. Likewise, based upon Belnap’s four-valued logic, Quadripartitioned Single-Valued Neutrosophic Sets (QSVNSs) are characterized by four independent components of truth, contradiction, ignorance, and falsity degrees to represent uncertain information or data at hand. In fact, QSVNSs are extensions of Single-Valued Neutrosophic Sets (SVNSs) where the indeterminacy component is further partitioned into two parts — contradiction and ignorance. However, QSVNSs alone cannot reflect the reliability measure of decision maker’s preferences or allocations. Thus, for better modeling of uncertainty and to combine the information of truth, contradiction, ignorance, and falsity degrees with their respective reliability attributes, we put forward a hybrid framework for the first time in this study. Hence, we propose a notion of Quadripartitioned Single-Valued Neutrosophic Z-number (QSVNZN), which is constructed as a generalization of the Z-number and the QSVNSs. Some basic operations and a score function are also defined for ranking QSVNZNs. Moreover, we also aggregate QSVN information with the help of three weighted aggregation operators viz., QSVNZN weighted arithmetic averaging (QSVNZNWAA) operator, QSVNZN weighted geometric averaging (QSVNZNWGA) operator, and QSVNZN weighted hybrid averaging (QSVNZNWHA) operator. Several suitable properties and relations between these operators are also presented. The applicability of our newly proposed operators and the score function is demonstrated in three multi-criteria decision making (MCDM) occasions specifically in the COVID-19 context. Meticulous comparative analysis, the validity of our proposed approaches, sensitivity analysis, and runtime analysis is also carried out to depict the legitimacy, veracity, and feasibility of our theoretical construct. The obtained results shall greatly benefit the decision-makers to deal with indeterminate and inconsistent information efficiently. Or in other words, the new framework shall exhibit sufficient descriptive ability from the human-cognition-based perspective.

A New Model of Interval-Valued Intuitionistic Fuzzy Weighted Operators and Their Application in Dynamic Fusion Target Threat Assessment.

Existing missile defense target threat assessment methods ignore the target timing and battlefield changes, leading to low assessment accuracy. In order to overcome this problem, a dynamic multi-time fusion target threat assessment method is proposed. In this method, a new interval valued intuitionistic fuzzy weighted averaging operator is proposed to effectively aggregate multi-source uncertain information; an interval-valued intuitionistic fuzzy entropy based on a cosine function (IVIFECF) is designed to determine the target attribute weight; an improved interval-valued intuitionistic fuzzy number distance measurement model is constructed to improve the discrimination of assessment results. Specifically, first of all, we define new interval-valued intuitionistic fuzzy operation rules based on algebraic operations. We use these rules to provide a new model of interval-valued intuitionistic fuzzy weighted arithmetic averaging (IVIFWAA) and geometric averaging (IVIFWGA) operators, and prove a number of algebraic properties of these operators. Then, considering the subjective and objective weights of the incoming target, a comprehensive weight model of target attributes based on IVIFECF is proposed, and the Poisson distribution method is used to solve the time series weights to process multi-time situation information. On this basis, the IVIFWAA and IVIFWGA operators are used to aggregate the decision information from multiple times and multiple decision makers. Finally, based on the improved TOPSIS method, the interval-valued intuitionistic fuzzy numbers are ordered, and the weighted multi-time fusion target threat assessment result is obtained. Simulation results of comparison show that the proposed method can effectively improve the reliability and accuracy of target threat assessment in missile defense.

Picture fuzzy Z-linguistic set and its application in multiple attribute group decision-making

Picture fuzzy linguistic set is a vital solution to express complex and uncertain information, which has been applied in multi-attribute group decision-making (MAGDM). However, the credibility of decision-making information is unconsidered, which may give rise to the inaccuracy of final result. To solve this problem, the picture fuzzy Z-linguistic set (PFZLS) composed of linguistic term, picture fuzzy number, and credibility is proposed, which could express more complete decision-making information. Subsequently, operation rules, comparison methods, and distance measures of PFZLS are introduced. In addition, the weighted geometric average operator and the classical VIKOR method are extended and combined to solve the MAGDM problem under the picture fuzzy Z-linguistic environment. Finally, an illustrative example about the emergency decision-making (EDM) problem of forest fire accident is proposed, and a series of comparative analyses are presented to verify the rationality and superiority of the PFZLS.

Some aggregation operators of credibility trapezoidal fuzzy neutrosophic values and their decision-making application in the selection of slope design schemes

Modern decision-making (DM) systems are becoming more and more complex and sophisticated in their demands for information expressions and credibility levels. In the existing literature, a trapezoidal fuzzy neutrosophic value (TFNV) that combines trapezoidal fuzzy numbers with neutrosophic values can be better depicted by truth, indeterminacy, and falsity membership functions. Unfortunately, TFNV implies its defect since it lacks a measure of credibility. To make TFNV more creditable, TFNV should be related to its credibility level. Regarding the motivation for combining TFNV with its credibility level, this paper first proposes the concept of a credibility TFNV (C-TFNV) as a new framework of TFNV associated with the measure of credibility. The advantage of its information expression is that C-TFNV has a more creditable ability to describe indeterminate and inconsistent knowledge and judgments of human beings by the mixed information of a TFNV and a related credibility level (an ordered pair of TFNVs). Next, we propose the operational laws of C-TFNVs and the score function of C-TFNV. Furthermore, we present a C-TFNV weighted arithmetic averaging (C-TFNVWAA) and a C-TFNV weighted geometric averaging (C-TFNVWGA) operators and their properties. Then, a multicriteria DM model based on the C-TFNVWAA and C-TFNVWGA operators and the score function is established in the case of C-TFNVs. Finally, an actual DM example of slope decision schemes is provided to show the applicability and efficiency of the established DM model in the case of C-TFNVs.

An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator

Capacity estimation is crucial for assessing the health statuses of lithium-ion batteries to develop better battery usage and maintenance strategies. However, it is difficult to satisfy multiple application situations in which each individual prognostic method has its own particular preconditions and application limitations. Thus, in this paper, an ensemble prognostic framework is proposed to integrate several individual prognostic methods to achieve better capacity estimation accuracy. In the proposed framework, a measurement- and calculation-based combined feature extraction method is first applied to the battery charging phase to better capture the extracted features that are related to the health statuses of lithium-ion batteries. Then, a novel validation dataset-based induced ordered weighted geometric averaging (V-IOWGA) operator is proposed to realize the time-varying weight allocation of each individual prognostic method to solve issue by which the performances of different prognostic algorithms vary in different phases. The advantages of the proposed ensemble model are verified on lithium-ion battery datasets from NASA PCoE and the University of Maryland CALCE Laboratory, and its prediction accuracy is better than that of other individual prognostic models. In addition, comparison experiments involving three types of ensemble models validate the superiority of the proposed model.

Identification and Classification of Aggregation Operators Using Bipolar Complex Fuzzy Settings and Their Application in Decision Support Systems

The idea of bipolar complex fuzzy (BCF) sets, as a genuine modification of both bipolar fuzzy sets and complex fuzzy sets, gives a massive valuable framework for representing and evaluating ambiguous information. In intelligence decision making based on BCF sets, it is a critical dilemma to compare or rank positive and negative membership grades. In this framework, we deliberated various techniques for aggregating the collection of information into a singleton set, called BCF weighted arithmetic averaging (BCFWAA), BCF ordered weighted arithmetic averaging (BCFOWAA), BCF weighted geometric averaging (BCFWGA), and BCF ordered weighted geometric averaging (BCFOWGA) operators for BCF numbers (BCFNs). To illustrate the feasibility and original worth of the diagnosed approaches, we demonstrated various properties of the diagnosed operators, in addition to their capability that the evaluated value of a set of BCF numbers is a unique BCF number. Further, multiattribute decision making (“MADM”) refers to a technique employed to compute a brief and dominant assessment of opinions with multiattributes. The main influence of this theory is implementing the diagnosed theory in the field of the MADM tool using BCF settings. Finally, a benchmark dilemma is used for comparison with various prevailing techniques to justify the cogency and dominancy of the evaluated operators.

Geometric Average Weakening Buffer GM(1,1) and its Application in Prediction of Employment Rate of College Graduates

Prediction of the employment rate of college graduates is very important not only for education department to make suitable decision but also for university to adjust its teaching plan. However, it is a difficult task to accurately predict the employment rate of college graduates because it is affected by many factors, making the original data fluctuant. In this paper, a simple and effective prediction approach is proposed to predict employment rate of college graduates using GM (1,1). To attenuate effect of data fluctuation and improve prediction accuracy of GM (1,1) model, the geometric average weakening buffer operator (GAWBO) is first applied to original employment rate data, and then GM (1,1) model is used to predict employment rate. The prediction experiment of Yanshan university employment rate from 2010 to 2019 is performed using the proposed method. Results show that the proposed method can obtain more accurate prediction result than GM (1,1). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Novel IMF SWARA-FDWGA-PESTEL Analysis For Assessment Of Healthcare System

Decision-making represents a very popular field with many developed approaches. However, still exists the need for the creation of novel integrated models such as well is the case in this paper. The novel integrated Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) method, Fuzzy Dombi weighted geometric averaging (FDWGA) operator and PESTEL (P-Political, E-Economic, S-Social, T-Technological, E-Environmental, L-Legal) model has been developed. Five decision-makers (DMs) have evaluated six main elements of the PESTEL analysis and 30 elements more (five for each group). In total, we have created 35 models based on the developed model. Results of PESTEL analysis based on IMF SWARA method and FDWGA shows that legal and economic factors represent the most significant parameters, while last placed belong environmental group. Also, the usefulness of the developed integrated model has been demonstrated.