Multi-attribute Group Decision-making Method Research Articles

Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively.

Maximum consensus model with individual tolerance and mixed DEA prospect cross-efficiency for multi-attribute group decision-making

Experts are mostly not completely rational, and they generally exhibit different psychological attitudes and behaviors in multi-attribute group decision-making (MAGDM), which may lead to actual decisions deviating from the results obtained by traditional objective methods. Therefore, it is reasonable and necessary to capture individual tolerance and risk attitudes of experts when dealing with MAGDM problems. To achieve this intention, we design a MAGDM method that combines the maximum consensus model with individual tolerance and mixed DEA prospect cross-efficiency. In the consensus reaching process (CRP), we devise a maximum consensus model with individual tolerance, which can achieve a high consensus level with limited expert tolerance. In the selection process, we first define the prospect aggressive and benevolent cross-efficiency functions. Then, a mixed DEA prospect cross-efficiency ranking method is proposed, which reflects the attitudes of DMs (decision-makers) towards losses and gains. Moreover, this method avoids DMs’ hesitation and trouble in choosing between aggressive and benevolent DEA models. Subsequently, the algorithm and framework for the MAGDM method based on individual tolerance and risk attitude are proposed. Finally, the applicability of our method can be validated by an illustrative example. Sensitivity analysis and comparative analysis are supplied to indicate the rationality and superiority of our developed method.

Determination of best renewable energy sources in India using SWARA-ARAS in confidence level based interval-valued Fermatean fuzzy environment

The requirement for renewable energy sources arises from the depletion of fossil fuels and the increasing energy demand. A case study in India has been conducted in this context to identify the most promising renewable energy sources. A novel multi-attribute group decision-making (MAGDM) method integrating stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) has been proposed for this purpose. We have used interval-valued Fermatean fuzzy numbers for data representation due to their ability to accommodate a broad range of fuzzy information. By introducing confidence level-based aggregation operators in an interval-valued Fermatean fuzzy environment, we avoid erroneous assessments by decision experts. We have employed objective and subjective weight determination approaches to determine decision experts’ weights. The SWARA method has been used for attribute weight evaluation due to its simplicity and efficiency, and the ARAS method is employed for alternative ranking and optimal choice selection using the utility degree. The findings show the potential to expand solar and wind power, whereas geothermal energy is not yet suitable for widespread deployment because of its early stage of development. We find the technical and environmental aspects to be especially significant during the decision-making process. To demonstrate the proposed method’s applicability, we have compared the results with existing MAGDM methods. To establish its stability, we conducted a sensitivity analysis. The presented MAGDM method can help make informed decisions regarding implementing solar energy as a primary source of energy supply in India, thereby aiding decision-making for policymakers, regulators, energy planners, and stakeholders.

Research on Case Reasoning Multi-attribute Group Decision-making Method Based on Hesitant Fuzzy Set

In this study, a hesitant fuzzy set-based case-based reasoning integration method is proposed for the multi-attribute group decision-making problem with unknown attribute weights and mixed forms of attribute values. First, from two perspectives, traditional distance measure and information theory, a multi-objective optimization model is constructed using the distance similarity measure and information entropy of each type of attributes to determine the attribute weights. Secondly, considering the hybrid and nonlinear characteristics of case data, based on the principle of symmetric interaction entropy and TOPSIS method, a global similarity measure based on symmetric interaction entropy is proposed and a case inference algorithm suitable for hesitant fuzzy environment is designed. Finally, by analyzing the arithmetic cases of the target case in the case base, the most similar historical cases to the target case are retrieved to determine the decision-making scheme, and the practicality and feasibility of the decision-making method are verified. The results show that considering hesitant fuzzy theory for case-based reasoning research will help improve the accuracy and reliability of decision-making and provide more effective support for multi-attribute group decision management.

Aczel–Alsina T-norm based group decision-making technique for the evaluation of electric cars using generalized orthopair fuzzy aggregation information with unknown weights

Data management and finding precise outcomes from large amounts of information are among the biggest challenges for scientists. The technique of multi-attribute group decision-making (MAGDM) is a valuable tool for investigating fuzzy data precisely. The key objective of the paper is to redefine the q-rung orthopair (q-RO) fuzzy set (FS) (q-ROFS) in the term of interval-valued and proposed new aggregation operators (AOs) based on the Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operations. The AA operational laws are a generalized form of existing TNs and TCNs and give more reliable results because they can fluctuate in their parametric values. The concept of interval-valued enlarges the space of membership degree (MD) and non-membership degree (NMD) for decision-makers. By taking qth power, the interval-valued q-ROFS (IV-q-ROFS) structure. The IV-q-ROFS can handle the uncertainty and vagueness in data, then interval-valued intuitionistic FS (IV-IFS) and interval-valued Pythagorean FS (PyFS) (IV-PyFS) and provide accurate results. The thought of power AOs (PAOs) makes the relationship between weight vectors and reduces the chances of uncertainty in aggregated results. By taking advantage of PAOs, this article is devoted to introducing the interval-valued q-ROF Aczel-Alsina power-weighted averaging (IV-q-ROFAAPWA) and interval-valued q-ROF Aczel-Alsina power-weighted geometric (IV-q-ROFAAPWG) operators. The fundamental axioms of AOs, idempotency, boundedness, and monotonicity, are also discussed. To illustrate the importance of suggested AOs, the real-life problem of electric car selection was solved by applying the MAGDM method using the proposed IV-q-ROFAAPWA and IV-q-ROFAAPWG operators. The comparison of proposed AOs with currently present AOs is also part of the article. We finally constructed solid conclusions.

A novel uncertain information modeling method based on cosine similarity and cross entropy under spherical uncertain linguistic fuzzy set

Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers’ (DMs’) cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs’ and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis.

A novel group decision-making method for interval-valued q-rung dual hesitant fuzzy information using extended power average operator and Frank operations

This paper advances the field of multi-attribute group decision making (MAGDM) by proposing a novel framework based on interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs). IVq-RDHFSs, which surpass most existing fuzzy sets, effectively represent complex fuzzy information by describing membership and non-membership degrees through interval value sets. However, prior MAGDM methods based on IVq-RDHFSs have been limited by the functions of operation rules and aggregation operators (AOs). This limitation is addressed through the construction of a new MAGDM framework, leveraging the robust Frank t-norm and t-conorm (FTT) operation and the extended power average (EPA) operator. The proposed framework features the interval-valued q-rung dual hesitant fuzzy Frank weighted extended power average (IVq-RDHFFWEPA) operator to obtain comprehensive evaluation values. The paper also introduces novel techniques for determining the weights of decision-makers and attributes. Practical applications of the proposed method are demonstrated through the assessment of desalination technology selection and rural green eco-tourism projects. Sensitivity and comparison analyses validate the superior functionality, accuracy, and flexibility of this method compared to many state-of-the-art methods. The contributions of this paper are two-fold: it develops efficient measurement techniques for IVq-RDHFSs, such as distance and weight calculation, and it introduces a comprehensive MAGDM method by integrating FTT and EPA under IVq-RDHFSs, which improves the efficiency of solving decision-making problems.

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

With rapid social and economic development, the process of population aging has increased the demand for community elderly healthcare service (CEHS) in China. However, the traditional government-oriented service supply cannot meet the various needs of CEHS, and it is critical to select a suitable supplier of CEHS to provide high-quality green services in the community. Therefore, this study focuses on the issue of green supplier selection of CEHS, explores an improved transformation method for processing multi-type data, and proposes an integrated method of multi-attribute group decision-making (MAGDM) which innovatively applies the degree of overall deviation measure (ODM) to determine expert weight. Finally, the effectiveness and accuracy of the new method are verified by experimental analysis. The results show that H2 is the top choice in the green supplier selection of CEHS, followed by H1, H4, H8, H5, H6, H3, H7, H12, H11, H9, and H10. In addition, the authors apply the traditional ED method to calculate expert weights and compare the results of ODM and ED. It is a fact that the improved ODM method should be more efficient and accurate than the traditional ED method.

A multi-attribute group decision making method based on novel distance measures and regret theory under probabilistic dual hesitant fuzzy sets

As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior.

Multiple-attribute group decision-making method of linguistic q-rung orthopair fuzzy generalized power average operator based on DEMATEL

Linguistic q-rung orthpair fuzzy sets (Lq-ROFSs) can facilitate describing the uncertainty and the vagueness information existing in the real world. Based on the advantages of Lq-ROFSs, this paper innovatively puts forward a new method to solve the multi-attribute group decision-making (MAGDM) problems when the attribute weight is completely unknown, and proves the feasibility and effectiveness of this method through illustrative examples. Firstly, we propose the linguistic q-rung orthopair fuzzy generalized power average (Lq-ROFGPA) operator, which considers not only the importance of the data itself, but also the interaction between the data, and prove its properties. In particular, the linguistic q-rung orthopair fuzzy weighted generalized power average (Lq-ROFWGPA) operator takes into account the weight between data, which can better aggregate evaluation information. Then, we introduce decision making trial and evaluation laboratory (DEMATEL) method of the linguistic q-rung orthpair fuzzy numbers (Lq-ROFNs) to analyze the causal relationship and key elements of complex systems. Based on DEMATEL method, we further develop a weight model to calculate the attribute weights, which can make up for the deficiency which is the influence of the interaction between attributes that the existing weight determination method for Lq-ROFNs does not consider. Finally, we present a new MAGDM method based on the Lq-ROFWGPA operator and DEMATEL method. Further, several practical examples are given to illustrate the effectiveness and superiority of this new method in comparison with other existing MAGDM methods.

A Multi-Attribute Group Decision-Making Method Based on Trust Relationship and DEA Regret Cross-Efficiency

In multiattribute group decision-making (MAGDM) problems, the data envelopment analysis (DEA) cross-efficiency method is an effective tool to evaluate each decision-making unit. However, this method usually assumes that the decision-maker (DM) is completely rational, which ignores the risk psychology of the DM in the decision-making process. To compensate for this deficiency, this article proposes an MAGDM method based on trust relationships and DEA regret cross-efficiency. First, we construct a modified model based on the CCR model. Then, we propose the DEA regret cross-efficiency method. This method involves the risk psychology of the DM in the decision-making process. Next, a weighting model is developed to aggregate the cross-efficiency comprehensive utility matrix. Subsequently, we construct trust propagation and trust aggregation models to obtain the weights of experts. Finally, the MAGDM method based on trust relationships and DEA regret cross-efficiency is designed to rank all alternatives. A numerical example is provided to illustrate the superiority and rationality of our proposed method.

Three-way decision-making methods with multi-intuitionistic β-neighborhood-based multiattribute group decision-making problems

Multiattribute group decision-making (MAGDM) is a decision analysis method used to address decision-making problems that have multiple attributes and multiple decision alternatives. It is generally believed that multigranulation rough sets (MGRSs) are a twofold scheme to solve MAGDM problems. Thus, in this paper, we aim to generalize MGRSs and decision-theoretic rough sets (DTRSs) to intuitionistic fuzzy β-covering (IFβC) information systems and propose a novel MAGDM method to handle decision-making problems, which not only deepens the cognition of the three-way decision (3WD) model but also expands its application in decision-making problems. The paper first introduces a novel conditional probability using the ideal positive degree of intuitionistic fuzzy numbers (IFNs) and then investigates its properties. Second, in the IFβC environment, we utilize the proposed conditional probabilities to construct two types of multigranulation decision-theoretic rough set (MGDTRS) models, an optimistic strategy and a pessimistic strategy, discuss their theoretical properties and explore the relationship between the two strategies. Third, a novel MAGDM method for multi-intuitionistic β-neighborhoods is established in accordance with the proposed MGDTRS model. We also establish an algorithm for the proposed MAGDM method. Fourth, we apply the proposed method to address several MAGDM problems. By conducting comparative analysis with existing methods, we demonstrate the effectiveness of the proposed method and discuss the advantages and limitations of the proposed method. Furthermore, we conduct extensive parameter experimentation analysis on the proposed method from different perspectives. The experimental results demonstrate the stability and effectiveness of our proposed method.

A multi-attribute group decision-making method based on data envelopment analysis under parallel expert evaluation system

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method.

An Extended TODIM Method and Applications for Multi-Attribute Group Decision-Making Based on Bonferroni Mean Operators under Probabilistic Linguistic Term Sets

Due to the complexity and uncertainty of decision-making, probabilistic linguistic term sets (PLTSs) are currently important tools for qualitative evaluation of decision-makers. The asymmetry of evaluation information can easily lead to the loss of subjective preference information for decision-makers, and the existing operation of decision-maker evaluation information fusion operators is difficult to solve this problem. To solve such problems, this paper proposes some new operational methods for PLTSs based on Dombi T-conorm and T-norm. Considering the interrelationships between the input independent variables of PLTSs, the probabilistic linguistic weighted Dombi Bonferroni mean Power average (PLWDBMPA) operators are extended and the properties of these aggregation operators are proposed. Secondly, the PLWDBMPA operator is used to fuse the evaluation information of decision-makers, avoiding the loss of decision information as much as possible. This paper uses social media platforms and web crawler technology to obtain online comments from users on decision-making to obtain the public’s attitude towards decision events. TF-IDF and Word2Vec are used to calculate the weight of alternatives on each attribute. Under traditional group decision-making methods and integrating the wisdom of the public, a novel multi-attribute group decision-making method based on TODIM method is proposed. Finally, the case study of Turkey earthquake shelter selection proves this method is scientific and effective. Meanwhile, the superiority of this method was further verified through comparisons with the PL-TOPSIS, PLWA, SPOTIS and PROMETHEE method.

The cross-border e-commerce platform selection based on the probabilistic dual hesitant fuzzy generalized dice similarity measures

Abstract Cross-border e-commerce platform (CBECP) plays a very important role in the development of a cross-border e-commerce (CBEC). How to select the best CBECP scientifically and reasonably is a very critical multi-attribute group decision-making (MAGDM) issue. With the uncertainty of people’s cognition of the objective world, the decision-making process is full of a lot of fuzzy information. In view of the great advantages of probabilistic dual hesitation fuzzy set (FS) in expressing decision-making information, and in combination with the very extensive use of the Dice similarity measure (DSM), a new MAGDM method is proposed for the optimal CBECP selection (CBECPS) under the probabilistic dual hesitation fuzzy (PDHF) environment. First, on the basis of reviewing a large number of documents on the CBECPS for CBEC, the evaluation index system for the CBECPS is constructed; second, several new DSMs are proposed in the PDHF environment; third, based on the two newly proposed probabilistic dual hesitant weighted generalized Dice similarity measures, two novel MAGDM methods are provided for CBECPS, which are used for CBECPS; finally, the two established MAGDM techniques are compared with the existing decision-making methods, and the parameter analysis is carried out to illustrate the effectiveness and superiority of the two established MAGDM techniques. The two established techniques can not only be used for CBECPS of CBEC, but also be extended to similar related research.

Supplier selection through interval type-2 trapezoidal fuzzy multi-attribute group decision-making method with logarithmic information measures

Supplier selection is a hot issue in logistics and procurement under the new economic situation. For the problems of supplier selection, the uncertainty and complexity of decision-making information often led to the mistakes in the process of group decision-making. In this paper, interval type-2 trapezoidal fuzzy (IT2TrF) information is used to deal with uncertainty and complex decision-making information, and a multi-attribute group decision making (MAGDM) method with IT2TrF information measures is designed for supplier selection problems. Specifically, we first present three axiomatic definitions for IT2TrF information measures. Then, based on logarithmic function, the IT2TrF logarithmic information entropy, the IT2TrF logarithmic similarity and the IT2TrF logarithmic cross-entropy are proposed to measure the fuzziness and similarity of decision-making information. In addition, an attribute weights determination method based on IT2TrF information entropy is established. Finally, a numerical example regarding assess the performance of suppliers is provided, and the comparative analysis demonstrates the applicability and feasibility of the developed MAGDM method.

MAGDM Model Using an Intuitionistic Fuzzy Matrix Energy Method and Its Application in the Selection Issue of Hospital Locations

Matrix energy is a valid mathematical tool for representing collective information. However, it is not used in the existing literature for fuzzy sets, intuitionistic fuzzy sets, and multi-attribute group decision making (MAGDM) problems, which highlights research gaps. Motivated by both matrix energy and the research gaps, this study aims to extend matrix energy to the energy of an intuitionistic fuzzy matrix (IFM) and to utilize the IFM energy method in the MAGDM problem, which fully contains all the IFM information on attribute weights, decision maker weights, and attribute values. To achieve these objectives, this paper first proposes IFM energy in terms of the true matrix energy and false matrix energy; then, it develops a MAGDM model using the IFM energy method and the score and accuracy equations of IFM energy. Then, the developed MAGDM model is applied to the selection problem of hospital locations in Shaoxing City, China to demonstrate the practicality and validity of the developed model. Compared with existing intuitionistic fuzzy MAGDM methods, the developed MAGDM model using the IFM energy method reveals its superiority and novelty in complete IFM information expressions and the MAGDM method because the existing MAGDM methods containing intuitionistic fuzzy set information have difficulty tackling MAGDM problems containing all the IFM information on attribute weights, decision maker weights, and attribute values.

Probabilistic dual hesitant fuzzy MAGDM method based on generalized extended power average operator and its application to online teaching platform supplier selection

Probabilistic dual hesitation fuzzy set (PDHFS) is a new tool used to enhance fuzzy and imprecise information in a changing and complex decision-making environment. Compared with the existing fuzzy sets, PDHFS is more suitable for identifying uncertain evaluation data in complex and realistic decision-making situations. The extended power average (EPA) and generalized power average (GPA) operators provide more information for the information aggregation process, reflecting the mutual support among all aggregation values. To solve the evaluation problems related to decision analysis involving extremely complex information, especially in the case of extreme data in the consideration of decision problems. In this paper, the generalized extended power average (GEPA) operator is proposed in the PDHF environment. This research has the following four important contributions. First, we propose a probability splitting algorithm of normalizing PDHFE, and give some new basic operations, score function, distance measures and aggregation operators. Second, to better integrate the advantages of EPA and GPA operators, we developed the GEPA operator, weighted form (WGEPA) and ordered form (GEPOWA). Meanwhile, by adjusting the parameter in three types of operators, we can find that many existing operators are special forms of the three types of operators. Third, extended the three types of operators to the PDHF environment, the PDHFGEPA, PDHFWGEPA, PDHFGEPOWA operators and some special aggregation operators are obtained, and studied their valuable properties in detail. Finally, taking the application of PDHFWGEPA operator as an example, a new multi-attribute group decision-making (MAGDM) technique is developed and used to solve the problem of online teaching platform supplier selection (OTPSS). Through parameter sensitivity analysis, the flexibility and stability of MAGDM technique are illustrated, and compared with some existing decision-making methods, the effectiveness of MAGDM method is proved. In addition, this technique also provides some theoretical references for dealing with other complex MAGDM problems, and provides a new operator for the extension of GEPA operator in other fuzzy environments. The biggest advantage of several operators is that they can pay full attention to the special role of extreme data in decision-making.

MAGDM method based on generalized hesitant fuzzy TODIM and cumulative prospect theory and application to recruitment of university researchers

The recruitment of university researchers can be considered a multi-attribute group decision-making (MAGDM) problem. MAGDM is a familiar issue with uncertainty and fuzziness in the decision-making field. Generalized hesitation fuzzy numbers (GHFNs) as a new expanded form of hesitation fuzzy numbers (HFNs) can better express the uncertain information in MAGDM. The TODIM is a very classical and widely used method to deal with the MAGDM issue. In this paper, we integrate cumulative prospect theory (CPT) into TODIM to consider not only decision makers’ subjective risk preferences but also their confidence level to obtain more reasonable choices under risk conditions. Therefore, we propose the GHF CPT-TODIM approach to tackle the MAGDM issue. Meanwhile, in the GHF environment, it is proposed to use the volatility of attribute information (entropy weighting method) to obtain the importance of attributes, obtain the unknown attribute weight, and enhance the rationality of weight information. Finally, the validity and usefulness of the technique are verified by applying the GHF CPT-TODIM technique to the recruitment of university researchers and comparing it with the existing GHF MAGDM method, which offers a new way to solve the MAGDM problem with GHFNs.

Multiattribute group decision making based on novel score function considering both the angle and fuzzy information under interval-valued intuitionistic fuzzy environment

The interval-valued intuitionistic fuzzy values (IVIFVs) have been widely employed for multiattribute group decision making (MAGDM). However, the existing decision making method using IVIFVs has some drawbacks. The recognition degree and information utilization suffer from a gap in the evaluation of alternatives. Therefore, the need for a reliable and effective decision method is obvious. In this paper, a novel score function considering both the angle and fuzzy information of IVIFVs is proposed, which ensures the effective fusion of the fuzzy information measure and angle measure and overcomes the shortcomings of the existing methods. Then, the weights of decision makers (DMs) are set as IVIFVs and subjective values are determined by the optimization model to ensure the objectivity and authenticity of the weights of DMs. Furthermore, a novel MAGDM method based on the proposed score function is proposed. Finally, the proposed method is implemented on the regional innovation resource allocation efficiency evaluation. The results show that our method possesses a higher recognition degree than the other methods, and it ensures relatively high ranking accuracy. The ideology and results of this research will help the DMs to evaluate the alternatives effectively.