Solving Group Decision-making Problems Research Articles

Group decision on rationalizing disease analysis using novel distance measure on Pythagorean fuzziness

Despite the fact that several technologies have been developed to assist healthcare workers in reducing errors and improving accuracy in illness diagnosis, there is still substantial ambiguity regarding the accurate disease diagnosis based on symptoms. The goal of this work is to establish a group decision-making problem in an uncertain situation to assist medical practitioners in generating accurate illness predictions based on symptoms. This study proposes a novel distance measure for Pythagorean fuzzy sets that incorporates the inherent uncertainty of complex, uncertain data by incorporating indeterminacy in the computation. First, we establish the proposed Mabala distance measure by describing it’s properties. Then, the suggested distance measure is applied to solve group decision-making problems in uncertain situations. A case study of disease analysis based on symptoms is presented to illustrate the decision-making procedure involving four medical professionals, five symptoms, and five probable diseases. Furthermore, We have presented two cases of disease analysis using non-standard and standard Pythagorean fuzzy soft matrices. The results suggest that the proposed Mabala’s distance measure has great potential for improving disease analysis. The proposed Mabala distance measure is compared to five existing distance measures using an identical data set of prospective disease symptoms. The comparative analysis indicates that the suggested Mabala distance measure’s result almost coincides with the results of the other distance measurements. A set of sensitivity analysis is provided to analyze the durability and consistency of the proposed distance measurements across different input scenarios.

Group decision-making method based on advanced aggregation operators with entropy and divergence measures under 2-tuple linguistic Pythagorean fuzzy environment

2-tuple linguistic Pythagorean fuzzy values (2TLPFVs) are flexible and more efficient tools to represent fuzzy linguistic information in complex real-world situations than 2-tuple linguistic values (2TLVs) and 2-tuple linguistic intuitionistic fuzzy values (2TLIFVs). This paper introduces a new score value function for 2TLPFVs and discusses its major properties. Two new aggregation operators (AOs) are presented, namely, the advanced 2-tuple linguistic Pythagorean fuzzy weighted average (Adv-2TLPFWA) operator and the advanced 2-tuple linguistic Pythagorean fuzzy weighted geometric (Adv-2TLPFWG) operator, which aggregate 2-tuple linguistic Pythagorean fuzzy information based on novel operational laws of 2TLPFVs. The work also exhibits several properties of these AOs in detail. Furthermore, the article discusses entropy and divergence measures under the 2-tuple linguistic Pythagorean fuzzy information environment. Finally, a new multiple attribute group decision-making (MAGDM) method is constructed to solve group decision-making problems in the 2-tuple linguistic Pythagorean fuzzy context. A real-life group decision-making problem of selecting the best green supplier for fruits and vegetables is investigated to verify the applicability and validity of the proposed approach. The sensitivity investigation and comparative analysis are conducted to illustrate the advantages of the proposed method.

Hamy mean operator under complex picture fuzzy environment and its application to disaster management program

Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies.

A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best–Worst Method

Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best–worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best–worst method (MCBWM) takes into account several linguistic terms for pairwise comparisons of relative preferences among the criteria. It has been shown that the MCBWM approach has advantages over BWM: it reduces the calculation and determines optimal weight values by providing the choices for the optimal solution. This paper proposes a unique method for group decision-making based on MCBWM. We extended the MCBWM to solve group decision-making problems. A novel solution approach was developed and validated for multiple problems. Two examples and one case study were solved using the proposed approach to demonstrate the validity and application of the proposed method. The results were further compared with existing models to validate the proposed approach. We found that the obtained ranking order for all problems is the same and that the proposed model has a higher consistency ratio than the existing approaches. This method can be extended to other mathematical programming models for collective decision making in uncertain situations.

An information measure based extended VIKOR method in intuitionistic fuzzy valued neutrosophic value setting for multi-criteria group decision making

The paper presents an extended VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for solving group decision-making problems. The uncertainties given in the data are handled with the help of the intuitionistic fuzzy valued neutrosophic values (IFVNVs), which allow decision-makers to carry more detailed information while providing their preferences in the imprecise environment. The proposed VIKOR method utilized the features of IFVNVs and computed the distance measures between their pairs using $L^p$-metric and $L^\infty$-metric. The weights of the different criteria are computed by using the entropy-based measures for the families of IFVNVs. The presented method has been illustrated with a numerical example. A comparative interpretation and the sensitivity analysis of the parameter associated with the technique are achieved to reveal their influences.

FP-intuitionistic multi fuzzy N-soft set and its induced FP-Hesitant N soft set in decision-making

Intuitionistic fuzzy sets (IFSs) can effectively represent and simulate the uncertainty and diversity of judgment information offered by decision-makers (DMs). In comparison to fuzzy sets (FSs), IFSs are highly beneficial for expressing vagueness and uncertainty more accurately. As a result, in this research work, we offer an approach for solving group decision-making problems (GDMPs) with fuzzy parameterized intuitionistic multi fuzzy N-soft set (briefly, FPIMFNSS) of dimension q by introducing its induced fuzzy parameterized hesitant N-soft set (FPHNSS) as an extension of the multi-fuzzy N-soft set (MFNSS) based group decision-making method (GDMM). In this study, we use the proposed GDMM to solve a real-life GDMP involving candidate eligibility for a single vacant position advertised by an IT firm and compare the ranking performances of the proposed GDMM with the Fatimah-Alcantud method.

A novel group decision making method for interval-valued pythagorean fuzzy preference relations

Interval-valued Pythagorean fuzzy preference relation (IVPFPR) plays an important role in representing the complex and uncertain information. The application of IVPFPRs gives better solutions in group decision making (GDM). In this paper, we investigate a new method to solve GDM problems with IVPFPRs. Firstly, novel multiplicative consistency and consensus measures are proposed. Subsequently, the procedure for improving consistency and consensus levels are put forward to ensure that every individual IVPFPR is of acceptable multiplicative consistency and consensus simultaneously. In the context of minimizing the deviations between the individual and collective IVPFPRs, the objective experts’ weights are decided according to the optimization model and the aggregated IVPFPR is derived. Afterwards, a programming model is built to derive the normalized Pythagorean fuzzy priority weights, then the priority weights of alternatives are identified as well. An algorithm for GDM method with IVPFPRs is completed. Finally, an example is cited and comparative analyses with previous approaches are conducted to illustrate the applicability and effectiveness of the proposed method.

Hybrid weighted aggregation operator of cubic fuzzy-consistency elements and their group decision-making model in cubic fuzzy multi-valued setting

The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model.

Optimisation of maintenance in delivery systems for cytostatic medicines

BackgroundThe real-world application of maintenance in organisations brings together a number of maintenance policies in order to achieve the desired availability, efficiency and profitability. However, the literature mostly chooses a single maintenance policy, and so the decision process is not suited to the real conditions in the company to which it is applied. Our study takes a combination of maintenance policies as alternatives, and so conforms to the actual practice of maintenance in organisations. Furthermore, it introduces the possibility of including extra spare parts, or outsourcing maintenance policies. Although the selection of maintenance policies has been applied to many kinds of business and of machine, there is almost no instance of its application to hospitals, and it has never been applied to delivery systems for cytostatic drugs.MethodsThe model uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is recognised as being highly suitable for solving group decision-making problems in a fuzzy environment. Fuzzy set theory is also considered to be more proficient than crisp numbers for handling the ambiguity, imprecisions, data scarcity, and uncertainty inherent in decisions made by human beings. The judgements required were obtained from a decision group comprising the heads of facilities maintenance, maintenance of medical equipment, health and safety at work, environment, and programming-admission. The group also included care staff; specifically, the heads of the main clinical services, and the medical supervisors. The model includes original criteria, such as Quality of health care, which measures impact on care as a function of mean availability of each alternative. It also considers Impact on hospital management via the criteria: Working environment in the organisation and Impact on health care; the former criterion measures equality among care services in the hospital, while the latter assesses the effect on regional health cover. The model was built using real data obtained from a state hospital in Spain. The model can also be easily applied to other national and international healthcare organisations, providing weights specific to the criteria. These are produced by a decision group from each healthcare organisation and the alternatives are updated in accordance with what is considered important in each hospital.ResultsThe results obtained from the model recommend changing the alternative that is currently in use, Corrective and Preventive Maintenance, to Corrective and Preventive Maintenance plus two spare hoods. This alternative would lead to an availability of 1 (the highest possible) in the systems for preparing personalised cytotoxic drugs, and so the quality of service is therefore very high. Additionally, it could offer services to all the users of the hospital, and also offer cover in the preparation of cytotoxic medicines to other hospitals in the catchment area.ConclusionsThe results suggest the possibility that improvements to the support and logistical systems, which include maintenance, traditionally held to have no effect on quality of care, may be key to improving care quality, but also in reducing risk to patients, care and non-care staff, and the environment.

A Decision-Making Approach Based on New Aggregation Operators under Fermatean Fuzzy Linguistic Information Environment

Fermatean fuzzy linguistic (FFL) set theory provides an efficient tool for modeling a higher level of uncertain and imprecise information, which cannot be represented using intuitionistic fuzzy linguistic (IFL)/Pythagorean fuzzy linguistic (PFL) sets. On the other hand, the linguistic scale function (LSF) is the better way to consider the semantics of the linguistic terms during the evaluation process. It is worth noting that the existing operational laws and aggregation operators (AOs) for Fermatean fuzzy linguistic numbers (FFLNs) are not valid in many situations, which can generate errors in real-life applications. The present study aims to define new robust operational laws and AOs under Fermatean fuzzy linguistic environment. To do so, first, we define some new modified operational laws for FFLNs based on LSF and prove some important mathematical properties of them. Next, the work defines several new AOs, namely, the FFL-weighted averaging (FFLWA) operator, the FFL-weighted geometric (FFLWG) operator, the FFL-ordered weighted averaging (FFLOWA) operator, the FFL-ordered weighted geometric (FFLOWG) operator, the FFL-hybrid averaging (FFLHA) operator and the FFL-hybrid geometric (FFLHG) operator under Fermatean fuzzy linguistic environment. Several properties of these AOs are investigated in detail. Further, based on the proposed AOs, a new decision-making approach with Fermatean fuzzy linguistic information is developed to solve group decision-making problems with multiple attributes. Finally, to illustrate the effectiveness of the present approach, a real-life supplier selection problem is presented where the evaluation information of the alternatives is given in terms of FFLNs. Compared to the existing methods, the salient features of the developed approach are (1) it can solve decision-making problems with qualitative information data using FFLNs; (2) It can consider the attitudinal character of the decision-makers during the solution process; (3) It has a solid ability to distinguish the optimal alternative.

Decision support model with Pythagorean fuzzy preference relations and its application in financial early warnings

Pythagorean fuzzy sets (PFSs) retain the advantages of intuitionistic fuzzy sets (IFSs), while PFSs portray 1.57 times more information than IFSs. In addition, Pythagorean fuzzy preference relations (PFPRs), as a generalization of intuitionistic fuzzy preference relations (IFPRs), are more flexible and applicable. The objective of this paper is to propose a novel decision support model for solving group decision-making problems in a Pythagorean fuzzy environment. First, we define the concepts of ordered consistency and multiplicative consistency for PFPRs. Then, aiming at the group decision-making problem of multiple PFPRs, a consistency improving model is constructed to improve the consistency of group preference relations. Later, a consensus reaching model is developed to reach the degree of group consensus. Furthermore, a decision support model with PFPRs is established to derive the normalized weights and output the final result. Holding these features, this paper builds a decision support model with PFPRs based on multiplicative consistency and consensus. Finally, the described method is validated by an example of financial risk management, and it is concluded that the solvency of a company is an important indicator that affects the financial early warning system.

Group decision making with hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic preference relations (HFLPRs) can be used to describe hesitant judgments of decision makers (DMs). To further research the utilization of HFLPRs, this paper develops a new group decision making (GDM) method with HFLPRs. First, a consensus checking method is proposed to measure the consensus level of individual HFLPRs. Then, a definition of acceptable consensus is introduced. Furthermore, to help experts whose consensus levels are below a predefined threshold to revise their preferences, a consensus reaching procedure is developed, where the acceptable multiplicative consistency of the revised individual HFLPRs is guaranteed. After that, a step-by-step algorithm is developed to help us solve GDM problems with HFLPRs. An application example is implemented to indicate the utilization of our method and to compare with previous research.

Algorithm for solving group decision-making problems based on the similarity measures under type 2 intuitionistic fuzzy sets environment

Type 2 intuitionistic fuzzy set (T2IFS) is one of the most important concepts to describe fuzzy information by providing an additional degree of freedom to the decision maker to decide the decision-making process. The fundamental superiority of the T2IFS, over the intuitionistic fuzzy set, has been its ability to capture the degrees of membership of relevant membership values, where the uncertainty is handled more accurately, and hence accommodate more uncertainties. Keeping these features in mind, this paper explores the theory of T2IFS by defining some families of the similarity measures to measure the degree of similarity between the two or more T2IFSs. For this, the characteristic of each object is measured in terms of degrees of primary membership and non-membership, secondary membership and non-membership and the variance margin function between them. Based on that, several formulations of the similarities measures are defined and their properties are investigated . Also, some fundamental relation between the proposed measures is stated. Further, based on the similarity measure, a group decision-making approach is explored to rank the alternatives. An illustrative example is provided to demonstrate the approach, and the comparative analysis with some of the existing measures is performed to explore the results.

Extended hesitant fuzzy linguistic term set with fuzzy confidence for solving group decision-making problems

This paper presents a new extension of the hesitant fuzzy linguistic term set (HFLTS) called intuitionistic fuzzy confidence-based HFLTS that associates an intuitionistic fuzzy value (IFV) with each linguistic term. The resulting term set is termed as intuitionistic fuzzy confidence hesitant fuzzy linguistic term set (IFCHFLTS). The previous studies on the linguistic decision making have emphasized little upon the preference and non-preference for each of the linguistic terms. This information, however, is crucial in multi-criteria decision making under uncertainty. In this regard, we find IFV particularly useful for qualifying each of the linguistic terms with the agent’s degree of preference, non-preference, and hesitation values. Besides, a new aggregation operator named intuitionistic fuzzy confidence linguistic simple weighted geometry (IFCLSWG) is also proposed to fuse decision makers’ linguistic preferences. Further, the criteria weights are estimated using a new method called intuitionistic fuzzy confidence linguistic standard variance. An approach is also suggested for ranking the given alternatives by adapting VIKOR under the proposed IFCHFLTS context. Finally, the practicality and usefulness of the proposal are demonstrated through two real-world problems in green supplier selection for manufacturing industry, and medical diagnosis. The strengths and weaknesses of the proposal are also highlighted by drawing upon a comparison with similar methods.

Solving group decision-making problems in manufacturing systems by an uncertain compromise ranking method

The purpose of this paper is to introduce a new modified compromise ranking method (VIKOR), known as sorting the possible alternatives and determining the compromise solution under interval-valued hesitant fuzzy sets (IVHFSs) for solving group decision-making problems in manufacturing systems. By using numerical measure and crisp data, the decision making is complex in real-life situations because the decision makers (DMs)' judgments are usually hesitant and vague. A more applicable and realistic approach based on interval-valued hesitant fuzzy elements (variables) is presented and defined as the inputs to the presented VIKOR method. The proposed interval-valued hesitant fuzzy modified VIKOR (IVHF-MVIKOR) method utilises the membership degrees to demonstrate the degrees of satisfiability for each possible alternative according to selected criteria for the manufacturing assessments. Then, a new index for relative importance of the DMs is introduced with the extended fuzzy sets. Also, some operations in the proposed IVHF-MVIKOR method are developed for manufacturing decision problems. Then, new indexes are presented with interval-valued hesitant fuzzy hamming distance measure for the purpose of rankings. Finally, by presenting a practical example in flexible manufacturing systems, the performance of IVHF-MVIKOR method is evaluated and compared with two well-known methods under interval-valued hesitant fuzzy information.

Solving group decision-making problems in manufacturing systems by an uncertain compromise ranking method

The purpose of this paper is to introduce a new modified compromise ranking method (VIKOR), known as sorting the possible alternatives and determining the compromise solution under interval-valued hesitant fuzzy sets (IVHFSs) for solving group decision-making problems in manufacturing systems. By using numerical measure and crisp data, the decision making is complex in real-life situations because the decision makers (DMs)' judgments are usually hesitant and vague. A more applicable and realistic approach based on interval-valued hesitant fuzzy elements (variables) is presented and defined as the inputs to the presented VIKOR method. The proposed interval-valued hesitant fuzzy modified VIKOR (IVHF-MVIKOR) method utilises the membership degrees to demonstrate the degrees of satisfiability for each possible alternative according to selected criteria for the manufacturing assessments. Then, a new index for relative importance of the DMs is introduced with the extended fuzzy sets. Also, some operations in the proposed IVHF-MVIKOR method are developed for manufacturing decision problems. Then, new indexes are presented with interval-valued hesitant fuzzy hamming distance measure for the purpose of rankings. Finally, by presenting a practical example in flexible manufacturing systems, the performance of IVHF-MVIKOR method is evaluated and compared with two well-known methods under interval-valued hesitant fuzzy information.

A New Intuitionistic Fuzzy Linguistic Hybrid Aggregation Operator and Its Application for Linguistic Group Decision Making

The linguistic aggregation operator is an important decision-making model that is proving effective for dealing with the input data that takes the form of uncertain information. In this paper, considering the principal component of the intuitionistic fuzzy linguistic variables, we develop a new intuitionistic fuzzy linguistic hybrid aggregation (NIFLHA) operator to solve group decision-making problems under the situation with intuitionistic fuzzy linguistic information. Then, we study some of its main properties by utilizing some operational laws of intuitionistic fuzzy linguistic variables and the different families of the NIFLHA operator. Moreover, the multiperson NIFLHA (MP-NIFLHA) operator is introduced to evaluate the opinions of experts. Finally, an illustrative example about a multiperson decision-making problem is developed to reveal the applicability and the availability of the raised operator.

Bonferroni means with distance measures and the adequacy coefficient in entrepreneurial group theory

The aim of the paper is to develop new aggregation operators using Bonferroni means, OWA operators and some distance measure. We introduce the BON-OWAAC and BON-OWAIMAM operators. We are able to include coefficient adequacy and the maximum and minimum levels in the same formulation with Bonferroni means and an OWA operator. The main advantages of using these operators are that they allow consideration of continuous aggregations, multiple comparisons between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Moore's families and Galois lattices to solve group decision-making problems. The professional and personal interests of the entrepreneurs who share co-working spaces are taken as an example for establishing relationships and groups. According to the professional and personal profile affinities for each entrepreneur, the results show dissimilarity and fuzzy relationships and the maximum similarity sub-relations to establish relationships and groups using Moore's families and Galois lattice. Finally, this new type of distance family can be used for applications in areas such as sports teams, strategy marketing and teamwork.

Multi-attribute group decision making approach based on interval-valued intuitionistic fuzzy sets and evidential reasoning methodology

In multi-attribute group decision making methods, team often needs to deal with both quantitative data and qualitative information with uncertainty. It is essential to properly represent and use uncertain information to conduct rational decision analysis. Having regarded this fact many approaches have been proposed for solving group decision-making problems, especially in fuzzy environments, but due to their drawbacks they get an unreasonable preference order of the alternatives in some situation. Thus in this paper based on interval-valued intuitionistic fuzzy sets and evidential reasoning methodology, a new approach has been proposed for supporting such decision situation. The experimental results are examined using the proposed approach. Computation steps and analysis results are provided to demonstrate its implementation process. The proposed method can overcome the drawbacks of the existing methods for fuzzy multi-attribute group decision making in intuitionistic fuzzy environments.

An algorithmic approach to group decision making problems under fuzzy and dynamic environment

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example.