Multi-attribute Group Decision Research Articles

Method for Pythagorean Interval 2‐Tuple Linguistic Multiattribute Group Decision Making and Its Application to the Ship Navigation Environment Safety Assessment

In this paper, some fundamental theories of the Pythagorean interval 2‐tuple linguistic numbers (PI2TLNs) combined with the EDAS (evaluation based on distance from average solution) method are proposed. Firstly, we introduced the original concepts of Pythagorean interval 2‐tuple linguistic sets (PI2TLSs), and two aggregation operators of PI2TLN were also introduced. Then, the steps of the EDAS method were described briefly. Moreover, the PI2TLN‐EDAS model was provided to address the multiattribute group decision‐making (MAGDM) problem, and calculation steps were deduced in detail. The effectiveness of the method was illustrated by taking the ship navigation environment safety assessment as an example. Finally, we conducted comparative analysis between PI2TLN‐EDAS method and Pythagorean interval 2‐tuple linguistic weighted averaging (PI2TLWA) operator/Pythagorean interval 2‐tuple linguistic weighted geometric (PI2TLWG) operator.

A new MAGDM method with 2-tuple linguistic bipolar fuzzy Heronian mean operators.

In this article, we introduce the 2-tuple linguistic bipolar fuzzy set (2TLBFS), a new strategy for dealing with uncertainty that incorporates a 2-tuple linguistic term into bipolar fuzzy set. The 2TLBFS is a better way to deal with uncertain and imprecise information in the decision-making environment. We elaborate the operational rules, based on which, the 2-tuple linguistic bipolar fuzzy weighted averaging (2TLBFWA) operator and the 2-tuple linguistic bipolar fuzzy weighted geometric (2TLBFWG) operator are presented to fuse the 2TLBF numbers (2TLBFNs). The Heronian mean (HM) operator, which can reflect the internal correlation between attributes and their influence on decision results, is integrated into the 2TLBF environment to analyze the effect of the correlation between decision factors on decision results. Initially, the generalized 2-tuple linguistic bipolar fuzzy Heronian mean (G2TLBFHM) operator and generalized 2-tuple linguistic bipolar fuzzy weighted Heronian mean (G2TLBFWHM) operator are proposed and properties are explained. Further, 2-tuple linguistic bipolar fuzzy geometric Heronian mean (2TLBFGHM) operator and 2-tuple linguistic bipolar weighted geometric Heronian mean (2TLBFWGHM) operator are proposed along with some of their desirable properties. Then, an approach to multi-attribute group decision-making (MAGDM) based on the proposed aggregation operators under the 2TLBF framework is developed. At last, a numerical illustration is provided for the selection of the best photovoltaic cell to demonstrate the use of the generated technique and exhibit its adequacy.

Decision self-information based on parameterized fuzzy β neighborhood and its application in three-way multi-attribute group decision-making

<abstract><p>As a special kind of entropy, decision self-information effectively considers the uncertainty information of both the lower and upper approximations. However, it is limited to rough binary relations, which limits its application to complex problems. In addition, parameterized fuzzy β covering, as an extension of the covering-based rough set model, can effectively characterize the similarity between samples. We combine decision self-information with a parameterized fuzzy β neighborhood to propose decision self-information in fuzzy environments, and we study its important properties. On this basis, a three-way multi-attribute group decision-making algorithm is established, and a practical problem is solved. The effectiveness of the proposed method is verified by experimental analysis.</p></abstract>

Integrated decision-making methods based on 2-tuple linguistic $ m $-polar fuzzy information

<abstract><p>The 2-tuple linguistic $ m $-polar fuzzy sets (2TL$ m $FSs) are acknowledged to represent the multi-polar information owing to the practical structure of $ m $-polar fuzzy sets with the help of linguistic terms. The TOPSIS and ELECTRE series are efficient and widely used methods for solving multi-attribute decision-making problems. This paper aim to augment the literature on multi-attribute group decision making focusing on the the strategic approaches of TOPSIS and ELECTRE-I methods for the 2TL$ m $FSs. In the 2TL$ m $F-TOPSIS method, the relative closeness index is used to rank the alternatives. For the construction of concordance and discordance sets, the superiority and inferiority of alternatives over each other are accessed by using the score and accuracy functions. In the 2TL$ m $F ELECTRE-I, selection of the best alternative is made by the means of an outranking decision graph. At the final step of the 2TL$ m $F ELECTRE-I method, a supplementary approach is developed for the linear ranking of alternatives based on the concordance and discordance outranking indices. The structure of the proposed techniques are illustrated by using a system flow diagram. Finally, two case studies are used to demonstrate the correctness, transparency, and effectiveness of the proposed methods for selecting highway construction project manager and the best textile industry.</p></abstract>

Extending COPRAS Method with Linguistic Fermatean Fuzzy Sets and Hamy Mean Operators

The utilization of linguistic variables is of vital importance for qualitative information processing in mathematical modelling and decision making under uncertainty. The primary purpose of our study is to introduce a novel structure by the fusion of Fermatean fuzzy sets and linguistic term sets. This hybrid structure is termed as the linguistic Fermatean fuzzy set which can be employed for dealing with decision‐making problems involving qualitative information. Several fundamental operations of linguistic Fermatean fuzzy sets are introduced. Furthermore, we present four classes of linguistic Fermatean fuzzy Hamy mean operators, namely, the linguistic Fermatean fuzzy Hamy mean operator, the linguistic Fermatean fuzzy dual Hamy mean operator, the linguistic Fermatean fuzzy weighted Hamy mean operator, and the linguistic Fermatean fuzzy weighted dual Hamy mean operator. Some basic properties of these linguistic Fermatean fuzzy Hamy mean operators are examined as well. In addition, we develop a linguistic Fermatean fuzzy extension of the COPRAS method. We also propose a novel approach to multi‐attribute group decision making with linguistic Fermatean fuzzy information. With our proposed methods, we solve two practical problems regarding food company ranking and green supplier selection, respectively. Finally, the efficacy of our developed method is validated through a comparative analysis with existing methods.

An Extension of Multi-Attribute Group Decision Making Method Based on Quantum-Like Bayesian Network Considering the Interference of Beliefs

An Extension of Multi-Attribute Group Decision Making Method Based on Quantum-Like Bayesian Network Considering the Interference of Beliefs

A personalized individual semantics-based multi-attribute group decision making approach with flexible linguistic expression

Multi-attribute group decision making (MAGDM) model which uncovers the preferences of individuals has become increasingly important in enhancing the efficiency of expert systems. Meanwhile, the use of flexible linguistic expression (FLE) and the personalized individual semantics (PIS) bring challenges to MAGDM. Therefore, in this paper, we devote to design a method based on personalized individual semantics for MAGDM with flexible linguistic expression, named PIS-based FLE-MAGDM. In the PIS-based FLE-MAGDM, we firstly develop a consistency-driven PIS model to derive personalized numerical scales (PNS) for linguistic terms with flexible linguistic preference relation (FLPR) and obtain the heterogeneous attribute weight by different decision makers. Then, we propose the individual selection process for getting the individual ranking of alternatives. Next, we provide the collective selection process to aggregate the individual ranking into the collective ranking. Finally, an application under the background of airline selection and some comparative analyses are presented to illustrate and justify the effectiveness of our proposals.

Taxonomy method for multiple attribute group decision making based on interval-valued intuitionistic fuzzy with entropy

Interval-valued intuitionistic fuzzy set (IVIFS) is a flexible method to deal with uncertainty and fuzziness. For the past few years, extensive researches about the multi-attribute group decision making (MAGDM) problems based on IVIFSs has been extensively studied in many fields. In this study, the Taxonomy method based on IVIFSs (IVIF-Taxonomy) was proposed for MAGDM problems. For the sake of the objectivity of attribute weight, entropy is introduced into the proposed model. The IVIF-Taxonomy method fully considers the weight of the decision makers (DMs) and the homogeneity of the chosen alternatives, making it more realistic. In addition, we apply IVIF-Taxonomy method to fund selection to verify the validity of IVIF-Taxonomy method. Finally, the trustworthy of IVIF-Taxonomy method is proved by comparing with the aggregate operator, IVIF-TOPSIS method, IVIF-GRA method and modified IVIF-WASPAS method.

The operational properties of linguistic interval valued q-Rung orthopair fuzzy information and its VIKOR model for multi-attribute group decision making

As a generalization of linguistic q-rung orthopair fuzzy set (Lq-ROFS), linguistic interval valued q-Rung orthopair fuzzy set (LIVq-ROFS) is a new concept to deal with complex and uncertain decision making problems which Lq-ROFS cannot handle. Due to the lack of information in decision making process, decision makers mostly prefer to give their preferences in interval form rather than a crisp number. In this situations, LIVq-ROFS appears up as a useful tool. In this work, we define operational laws of LIVq-ROFS and prove some properties. Furthermore, we propose the conception of the LIVq-ROF weighted averaging operator and give its formula by mathematical induction. To compare two or more linguistic interval valued q-Rung orthopair fuzzy numbers (LIVq-ROFNs), the improved form of score function is also given. Considering the powerfulness of LIVq-ROFSs handling ambiguity and complex uncertainty in practical problems, the key innovation of this paper is to develop the linguistic interval-valued q-rung orthopair fuzzy VIKOR model that is significantly different from the existing VIKOR methodology. The computing steps of this newly created model are briefly presented. Finally, the effectiveness of model is verified by an example and through comparative analysis, the superiority of VIKOR method is further illustrated.

Single-valued neutrosophic TODIM method based on cumulative prospect theory for multi-attribute group decision making and its application to medical emergency management evaluation

In recent years, emergent public health events happen from time to time, which puts forward new requirements for the establishment of a perfect medical emergency system. It is a new direction to evaluate the effectiveness of medical emergency systems from the perspective of multi-attribute group decision making (MAGDM) issues. In such article, we tend to resolve the MAGDM issues under single-valued neutrosophic sets (SVNSs) with TODIM method based on cumulative prospect theory (CPT). And the single-valued neutrosophic TODIM method based on CPT (CPT-SVN-TODIM) for MAGDM issues are developed. This new method not only inherits advantages of classical TODIM method, but also has further improvement in some aspects. For example, we set up the entropy to calculate attribute weights for ensuring the more objective decision-making process. Furthermore, it is also an extension of MAGDM method to utilize single-valued neutrosophic numbers (SVNNs) to depict decision makers’ ideas. In addition, we introduce the application of CPT-SVN-TODIM method in the assessment of medical emergency management. And finally, the reliability of CPT-SVN-TODIM method is confirmed by comparing with some other methods.

A probabilistic hesitant fuzzy Choquet integral-based TODIM method for multi-attribute group decision-making

A probabilistic hesitant fuzzy set (PHFS) is a generalization of the hesitant fuzzy set (HFS) that adds the probability of occurrence to each possible value of the hesitant fuzzy element (HFE). It is a powerful tool for handling uncertain information, especially when there is no complete consensus or full dissensus among decision-makers. The TODIM (an acronym in Portuguese for interactive and multiple attribute decision-making) approach is a multi-attribute decision-making (MADM) technique based on prospect theory. In this paper, we developed a Choquet integral-based TODIM method under a probabilistic hesitant fuzzy environment. Firstly, we introduced both a geometric score function and a geometric variance function of the probabilistic hesitant fuzzy element (PHFE). Furthermore, we proposed a novel comparison law between two PHFEs, and we defined a new distance measure of PHFEs. Considering the interaction between the criteria, we calculated the degree of importance of the criteria with the Shapley value. Next, we introduced the principles and steps of the developed TODIM method. One of the prominent components of the TODIM method is its risk aversion coefficient. However, it has not been sufficiently addressed in the research literature. In this regard, a novel optimization-based method is proposed to calculate the attenuation factor of the TODIM method. Finally, we verify the effectiveness of the Choquet integral-based probabilistic hesitant fuzzy-TODIM (PHF-TODIM) method using a supplier selection problem in the dairy industry.

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi-attribute group decision-making method with multi-parametric distance measures.

The policy 'hierarchical medical treatment system' promulgated by the State Council of China is an effective way to solve the problem of insufficient and unbalanced medical resources. In response, governments in different provinces explore a variety of different strategies to promote this policy, producing different results. To better strengthen the policy development, it is worthy to help policy-makers make decisions to elect the best one from different proposals. Thus, the aim of this paper is to develop a multi-attribute group decision-making (MAGDM) framework to better assist government select the optimal proposal. This study proposes a MAGDM method based on a family of q-rung orthopair fuzzy interaction power point Hamy mean operators to solve the above problem. To this end, new multi-parametric distance measures based on point operators in the framework of q-rung orthopair fuzzy set are proposed. With the help of the point distance measures, new power point operators average operator is also proposed. The results show that the proposed MAGDM method in this paper outperforms some existing methods and provides promising results for policy-makers seeking to identify the optimal hierarchical diagnosis and treatment policy (HDTP) proposal. Specifically, the results also revealed the best proposal for developing the HDTP proposals is Xiamen mode.

Pythagorean Fuzzy Soft Einstein Ordered Weighted Average Operator in Sustainable Supplier Selection Problem

Pythagorean fuzzy soft set (PFSS) is the most influential and operative extension of the Pythagorean fuzzy set (PFS), which contracts with the parametrized standards of the substitutes. It is also a generalized form of the intuitionistic fuzzy soft set (IFSS) and delivers a well and accurate estimation in the decision-making (DM) procedure. The primary purpose is to prolong and propose ideas related to Einstein’s ordered weighted aggregation operator from fuzzy to PFSS, comforting the condition that the sum of the degrees of membership function and nonmembership function is less than one and the sum of the squares of the degree of membership function and nonmembership function is less than one. We present a novel Pythagorean fuzzy soft Einstein ordered weighted averaging (PFSEOWA) operator based on operational laws for Pythagorean fuzzy soft numbers. Furthermore, some essential properties such as idempotency, boundedness, and homogeneity for the proposed operator have been presented in detail. The choice of a sustainable supplier is also examined as an essential part of sustainable supply chain management (SSCM) and is considered a crucial multiattribute group decision-making (MAGDM) issue. In some MAGDM problems, the relationship between alternatives and uncertain environments will be the main reason for deficient consequences. We have presented a novel aggregation operator for PFSS information to choose sustainable suppliers to cope with those complex issues. The Pythagorean fuzzy soft number (PFSN) helps to represent the obscure information in such real-world perspectives. The priority relationship of PFSS details is beneficial in coping with SSCM. The proposed method’s effectiveness is proved by comparing advantages, effectiveness, and flexibility among the existing studies.

Intuitionistic principal value Z-linguistic hybrid geometric operator and their applications for multi-attribute group decision-making

Intuitionistic principal value Z-linguistic hybrid geometric operator and their applications for multi-attribute group decision-making

Research on Investment Environment Performance Evaluation of Blockchain Industry with Intuitionistic Fuzzy CODAS Method

With global resource waste and environmental pollution becoming increasingly serious, corporate investment environmental performance (CIEP) of blockchain industry has received much attention from researchers over the past decade. As an important part of economic development, enterprises also pay increasing attention to environmental protection and pollution control. CIEP of blockchain industry is regarded as the result of corporate environmental management. Assessing CIEP of blockchain industry can not only make enterprises focus on the environmental protection and management but also promote sustainable social development. Also, it is always viewed as multiattribute group decision making (MAGDM). Thus, a new MAGDM model is used to tackle it. Based on the CODAS (combinative distance-based assessment) method and intuitionistic fuzzy sets (IFSs), this paper designs the distance-based IF-CODAS method to assess CIEP. Eventually, an application about CIEP evaluation is employed to show the superiority of the defined method. The results illustrate that the defined framework is very useful for assessing CIEP.

A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment

This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs’ prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model.

Enhancing ELECTRE I Method with Complex Spherical Fuzzy Information

This article is concerned to delineate the strategic approach of ELiminating Et Choice Translating REality (ELECTRE) method for multi-attribute group decision-making (MAGDM) in terms of complex spherical fuzzy sets. The feasible, well-suited, and marvelous structure of complex spherical fuzzy set compliments the decision-making efficiency and ranking calibre of ELECTRE I approach to present a beneficial and supreme aptitude strategy for MAGDM. Beside the proposed methodology, a few non-fundamental properties of complex spherical fuzzy weighted averaging (CSFWA) operator inclusive of shift invariance, homogenous, linearity, and additive property are also explored. The proposed procedure validates the individual opinions into an acceptable form by the dint of CSFWA operator and the aggregated opinions are further analyzed by the proposed complex spherical fuzzy- ELECTRE I (CSF-ELECTRE I) method. Within the consideration of proposed methodology, normalized Euclidean distances of complex spherical fuzzy numbers are also contemplated. In CSF-ELECTRE I method, the score, accuracy, and refusal degrees determine the concordance and discordance sets for each pair of alternatives to calculate the concordance and discordance indices, respectively. Based on aggregated outranking matrix, a decision graph is constructed to attain the ELECTREcally outranked solutions and the best alternative. This article provides supplementary approach at the final step to profess a linear ranking order of the alternatives. The versatility and feasibility of the presented method are embellished with two case studies from the business and IT field. Moreover, to ratify the intensity and aptitude of the presented methodology, we provide a comparative study with complex spherical fuzzy-TOPSIS method.

Proportional hesitant 2‐tuple linguistic distance measurements and extended VIKOR method: Case study of evaluation and selection of green airport plans

Building green airports can be regarded as among the most promising routes to sustainable development of ecosystems and human health. This study aims at addressing the problem of green airport plan selection under an uncertain context by developing an uncertain multiattribute group decision making (MAGDM) model. In the proposed model, the assessment information is characterized in the form of a proportional hesitant 2-tuple linguistic term set (PH2TLTS), which incorporates in binary form linguistic information that can accurately quantify subjective assessment information provided under uncertainty. The weights of assessment attributes of green airport plans are obtained automatically through a nonlinear programming model, which enhances the robustness of the decision-making method. Subsequently, on the basis of PH2TLTSs, three distance measures are proposed: the proportional hesitant 2-tuple linguistic Jaccard distance (PH2TLJD), the supplementary proportional hesitant 2-tuple linguistic normalized Minkowski distance (SPH2TLNMD) and the cluster-based proportional hesitant 2-tuple linguistic normalized Minkowski distance (CBPH2TLNMD). The TOPSIS-based comparison method proposed here can better determine the priorities of PH2TLTSs. The ranking and selection of green airport plans are derived using the PH2TL-VIKOR model. Finally, a case study accompanied by sensitivity and comparative analyses is performed to verify the rationality and feasibility of the proposed model.

Development of a Group Decision Support System with the Multi-Stage Multi-Attribute Group Decision Making (MS-MAGDM) Method on the Intelligent Warehouse Management System

Purpose: to find a solution with MS-DAGDM for the problem of different criteria used by decision maker at each stage.Design/methodology/approach: This research was conducted using literature review with a study of the theory of decision-making methods, group decisions, suplier selection processes, and factors that influence decisions in the context of warehousing and MS-MAGDM to solve the problems.Findings/result: This research find that GDSS prototypes which have four methods in making decisions. First, Analytical Hierarchy Process for weighting the division head level. Second, TOPSIS for divison head level decisions. Third, Hybrid Weight Averaging (HWA) manager level. Fourth, Time Weight Averaging (TWA) for manager level decisions.Originality/value/state of the art:The decision-making model of the GDSS system in this study combines four methods at each level of management. The section head level uses AHP for the level weighting and TOPSIS for decision making. Level managers use Hybrid Weight Averaging (HWA) weighting and Time Weight Averaging (TWA) for decisions. The combination of these methods is carried out using a Poisson distribution, for HWA and TWA operators to combine individual decisions into group decisions. Tujuan: Fokus penelitian ini adalah mencari solusi dengan MS-MAGDM untuk permasalahan perbedaan kriteria yang dipergunakan pembuat keputusan dalam setiap stage.Perancangan/metode/pendekatan: Metode yang digunakan yaitu kajian kepustakaan dengan kajian terhadap teori metode pembuatan keputusan, keputusan kelompok, proses pemilihan supplier, dan faktor yang berpengaruh pada keputusan dalam konteks pergudangan serta MS-MAGDM untuk menyelesaikan permasalahan tersebut.Hasil: Hasil penelitian ini berupa purwarupa GDSS yang memiliki 4 metode dalam pembuatan keputusan yaitu Analytical Hierarchi Process (AHP) untuk pembobotan level kepala bagian, TOPSIS untuk keputusan level kepala bagian, Hybrid Weight Averaging (HWA) pembobotan pada level manager dan Time Weight Averaging (TWA) untuk keputusan level managerKeaslian/ state of the art:Model pengambilan keputusan sistem GDSS penelitian ini menggabungkan 4 metode pada setiap tingkatan manajemen. Level kepala bagian menggunakan AHP untuk pembobotan level dan TOPSIS untuk pembuatan keputusan. Level manager menggunakan Hybrid Weight Averaging (HWA) pembobotan dan Time Weight Averaging (TWA) untuk keputusan. Penggabungan metode dilakukan menggunakan distribusi Poisson, untuk operator HWA dan TWA guna memadukan keputusan individu mejadi keputusan kelompok.

Multi-attribute group decision-making based on Bonferroni mean operators for picture hesitant fuzzy numbers

Multi-attribute group decision-making based on Bonferroni mean operators for picture hesitant fuzzy numbers