Group Decision-making Method Research Articles

An Improved Laplacian Gravity Centrality-Based Consensus Method for Social Network Group Decision-Making with Incomplete ELICIT Information

With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), which has garnered considerable attention in recent years. However, most existing consensus-reaching methods in SNGDM only consider local network information when determining the influence of DMs within the social network. This approach fails to adequately reflect the crucial role of key DMs in regulating information propagation during the consensus-reaching process. Additionally, the partial absence of linguistic evaluations in the decision-making problems also poses obstacles to identifying the optimal alternative. Therefore, this paper proposes an improved Laplacian gravity centrality-based consensus method that can effectively handle incomplete decision information in social network environments. First, the extended comparative linguistic expressions with symbolic translation (ELICIT) are utilized to describe DMs’ linguistic evaluations and construct the incomplete decision matrix. Second, the improved Laplacian gravity centrality (ILGC) is proposed to quantify the influence of DMs in the social network by considering local and global topological structures. Based on the ILGC measure, we develop a trust-driven consensus-reaching model to enhance group consensus, which can better simulate opinion interactions in real-world situations. Lastly, we apply the proposed method to a smart city evaluation problem. The results show that our method can more reasonably handle incomplete linguistic evaluations, more comprehensively capture the influence of DMs, and more effectively improve group consensus.

Interval-valued (p,q,r)-spherical fuzzy sets and their applications in MCGDM and MCDM based on TOPSIS method and aggregation operators

The (p,q,r)-Spherical fuzzy set is a useful generalization of other fuzzy structures such as fuzzy, intuitionistic fuzzy, Pythagorean fuzzy, picture fuzzy, q-rung orthopair fuzzy, spherical fuzzy, and T-spherical fuzzy sets. In this paper, we define concept of interval-valued (p,q,r)-spherical fuzzy set (IVpqr-SFS) and Set-theoretical and arithmetic operations of them. Two aggregation operators named interval-valued (p,q,r)-spherical fuzzy weighted arithmetic (IVpqr-SFWAA) and interval-valued (p,q,r)-spherical fuzzy weighted geometric (IVpqr-SFWGA) aggregation operators are introduced for IVpqr-SF numbers (IVpqr-SFN) and their properties were examined. The proposed operators are explained with examples. Furthermore, the score and accuracy functions of an IVpqr-SFN are introduced. We also define distance measures between two same types IVpqr-SFSs based on Hamming, Euclidean, and Hausdorff distance measures. Moreover, a multi-criteria group decision-making (MCGDM) method is developed based on the TOPSIS method by using the proposed distance measures and score function, and a numerical example is given to show the process of the proposed MCGDM method. In addition, a multi-criteria decision-making (MCDM) method is developed using the proposed IVpqr-SFWAA, IVpqr-SFWGA operators, and an illustrative example is given to show the process of the proposed MCDM method. Finally, a comparative analysis is presented between the proposed methods and the existing methods.

A PSO-algorithm-based dual consensus method for large-scale group decision making and its application in medical imaging equipment purchasing

The increasing complexity of social activities requires an expanding number of people to be involved in decision-making, so that the large-scale multi-attribute group decision-making problems have gained widespread attention. After overviewing the researches of large-scale multi-attribute group decision-making methods, we have found that: The existing methods only consider consensus based on evaluation information of alternatives, while ignoring the consensus on importance of attributes. Thus, in order to tackle the issues and describe the hesitancy of decision makers in the decision-making process, this paper proposes a novel dual consensus method in large-scale multi-attribute group decision making under hesitant fuzzy linguistic environment. In the consensus reaching process, the method considers not only the consensus on the evaluation information of alternatives, but also the consensus on the importance of attributes, where both consensus reaching processes are implemented by the particle swarm optimization algorithm. The subjective weights of attributes are derived from the consensus reaching process of preference matrices, and the objective weights of attributes are obtained from the consensus reaching process of decision matrices, so as to acquire the comprehensive weights. After that, the overall ranking of alternatives can be obtained by TODIM. Finally, the proposed method is applied to a case study of medical imaging equipment purchasing decision-making, and the comprehensive analysis is provided to clarify advantages of the proposed method.

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Soft Information for Medical Diagnosis Model

The medical diagnosis of many critical diseases is difficult as it usually requires the combined effort of several doctors. At this time, the process of medical diagnosis is actually a group decision-making (GDM) problem. In group medical diagnosis, considering doctors’ weight information and fusing the interaction relation of symptoms remain open issues. To address this problem, a group decision-making method for intuitionistic fuzzy soft environments is proposed for medical diagnosis because the intuitionistic fuzzy soft set (IFSS) integrates the advantages of the soft set and intuitionistic fuzzy set (IFS). Intuitionistic fuzzy soft weighted Muirhead mean operators are constructed by combining Einstein operations with the Muirhead mean (MM) operator, and some properties and results are revealed. A group medical diagnosis model with unknown doctor weight information and incomplete intuitionistic fuzzy soft information is proposed. Similarity measures of the intuitionistic fuzzy soft matrix (IFSM) given by the doctors are used to estimate the incomplete information. To take into account the advantages of objective weight and subjective weight, the combined weights of doctors are calculated based on the IFSMs’ similarity measure and doctors’ grades. The developed operators are then used to combine the evaluation information and handle the correlation of input arguments in the group medical diagnosis process. Finally, a numerical problem is selected to illustrate the superiority of the proposed approach compared to related methods. The combined weights are determined to overcome the shortcomings of the single-weight method to some extent. Meanwhile, the proposed method is more comprehensive, and can provide more flexible and reasonable choices for group medical diagnosis problems.

A utility-based three-way group decision consensus model with overlapping subgroups

This paper introduces a relative utility function-based consensus method for three-way group decision (TWGD) in a social network scenario where overlapping subgroups and non-overlapping subgroups co-exist. Firstly, we construct a three-way decision (TWD) model rooted in utility theory, which proposes a novel objective calculation method for the relative utility functions based on evaluation values. The relative utility matrices of decision makers (DMs) are derived on this basis. Secondly, a utility and trust-driven overlapping clustering algorithm is designed to maximize the overall trust level and the difference between subgroup clustering centers, aiming to divide the group into several manageable subgroups. In addition, we identify contrarian DMs, triggering a delegation-exit mechanism, and determine the weights of subgroups and DMs. Thirdly, a three-way group consensus decision model is formulated to provide targeted adjustment strategies for different types of DMs. To improve group consensus, we address the influence of key DMs on the opinions of overlapping subgroups with a minimum adjustment consensus model. Finally, we demonstrate the feasibility and scientificity of the proposed method through the illustrative example, sensitivity analysis, and comparative analysis.

Knowledge-based decisions for asset management of urban water infrastructure in Iran and Brazil using a nominal-based multi-criteria group decision method

ABSTRACT Asset management of the Urban Water Infrastructure (UWI) is typically faced with limitations regarding uncertain operational data. This shortcoming can be improved using the knowledge-based decisions of experienced experts. However, since knowledge-based decisions depend on decision-makers’ knowledge and experience, verifying their results in various regions is essential. This subject was addressed in this research. Hence, four groups of experts in Iran and Brazil collaborated to assess the criteria effective in the Maintenance-Rehabilitation Planning (MRP) of urban water pipes. For this purpose, a nominal-based multi-criteria group decision-making method was developed. To verify the results of this method, the viewpoints of non-weighted and weighted decision-makers were investigated and compared pairwise. The findings of this research improved the asset management of the UWI in Iran and Brazil by identifying the criteria applicable to the MRP of the pipes based on knowledge-based decisions. Likewise, it was found that weighting the experts has no decisive effect on the results and is only effective in groups where the decision-makers’ weights are identically distributed. Moreover, it was shown that there is a relationship between the similarity of results and the similarity of experts’ profiles in knowledge-based decision groups of various countries such as Iran and Brazil.

Application of Multi-Criteria Group Decision-Making Method in Interior Decoration Service

<p>值此消費者導向時代，室內設計公司應秉持以客為尊，針對客戶住宅之條件做出最適合之裝修建議，為客戶提供最完整，最優質的室內裝修服務，從而為公司贏得比其他公司更好的聲譽。五項與選擇天花板、壁面及地坪裝修有關之評估準則，依次藉由專家訪談、初步問卷及模糊德菲法(fuzzy Delphi method, FDM)取得。本研究以3個業主住宅之天花板、壁面及地坪之12個裝修項目做為研究對象，採用層級分析法(analytic hierarchy process, AHP)結合偏好順序評價法(technique for order preference by similarity to ideal solution, TOPSIS)及模糊偏好順序評價法(fuzzy TOPSIS)兩種不同分析方法，對每個裝修項目中之各替代方案進行擇優排名。12個裝修項目中，對於不同業主採用AHP結合TOPSIS及fuzzy TOPSIS兩種不同方法計算之結果顯示，共有客餐廳天花板、臥房天花板、客餐廳壁面及臥房地坪等四個裝修項目所得各替代方案之排名順序皆相同。對於廚房天花板及浴室壁面之裝修，同一業主採用AHP結合TOPSIS 及fuzzy TOPSIS兩種不同方法所得各替代方案之排名順序皆相同。兩種不同分析方法對於家庭成員僅有二人之業主3所得排名結果差異不大。本研究認為家庭成員愈多之大家庭在替代方案之排名順序應該以更具有參考價值之fuzzy TOPSIS所得之結果為準。</p> <p> </p><p>In this consumer-oriented era, interior design companies should adhere to the principle of customer-oriented, make the most suitable suggestions for the conditions of customers’ houses decoration, and provide customers with the most complete and high-quality interior decoration services, so as to win more good reputation than other companies. Five evaluation criteria related to the selection of ceiling, wall and floor decoration were obtained through expert interviews, preliminary questionnaires and fuzzy Delphi method in turn. This study takes 12 decoration projects of ceilings, walls and floors of 3 owners’ residences as the research objects, using the analytic hierarchy process (AHP) combined with the technique for order preference by similarity to ideal solution (TOPSIS) and the fuzzy TOPSIS methods to rank the alternatives in 12 decoration projects. The identical ranking order results were obtained for three owners by AHP combined with TOPSIS and fuzzy TOPSIS in the ceiling of the guest dining room, the ceiling of the bedroom, the wall of the guest dining room, and the floor of the bedroom. For the decoration of kitchen ceilings and bathroom walls, the same owner uses AHP combined with TOPSIS and fuzzy TOPSIS two different methods to obtain the ranking order of the alternatives are the same. There is little difference in the ranking results obtained by the two different analysis methods for owners 3 with only two family members. This study believes that the ranking order of alternative programs for large families with more family members should be based on the results obtained by fuzzy TOPSIS, which has more reference value.</p> <p> </p>

A two-stage stochastic preference analysis on group decision making with interval additive preference relations

Interval additive preference relation (IAPR) is an efficient tool for describing the uncertain pairwise comparisons between alternatives in group decision-making (GDM). Existing GDM methods with IAPRs view the preference value as a whole and establish concrete conflict/consensus mechanisms for fusing individual opinions while neglecting the stochastic preference and aggregation modes to meet the different demands of decision. Thus, to reflect the stochastic characteristic in both preference representation and group preference aggregation, this paper presents a novel two-stage stochastic preference analysis on GDM with IAPRs. In first stage, the method involves extracting stochastic additive preference relations (SAPRs) from the IAPRs. A preference relations matrix is then established using a logarithmic least squares model applied to the SAPRs. In second stage, the weighted preference matrix is synthesized by combining the preference relations matrix with the decision makers’ weight space. This leads to the computation of positive and negative ideal preference solutions, as well as positive and negative preference distances. Subsequently, the relative preference closeness is calculated based on these distances. Relative ranking results are determined by an algorithm evaluating pairwise comparisons among alternatives, resulting in transitional preference rankings. The second stage involve deriving ranking acceptance indices and ranking confidence degrees through a continuous extraction process, facilitating the establishment of the ultimate best preference rankings. The proposed method’s effectiveness and validity are demonstrated by numerical examples and Monte Carlo simulations.

A new multi-attribute group decision-making method based on Einstein Bonferroni operators under interval-valued Fermatean hesitant fuzzy environment

Faced with the increasing complexity and uncertainty of decision-making information, interval-valued Fermatean hesitant fuzzy sets (IVFHFSs) were presented as a novel mathematical model that handled uncertain data more effectively. However, existing multi-attribute group decision-making (MAGDM) methods based on IVFHFSs do not thoroughly investigate the operational laws. Also, these existing MAGDM methods do not take into account the connections between attributes and are less flexible. To address these issues, this paper proposes a new MAGDM method based on Einstein Bonferroni operators under IVFHFSs. First, we thoroughly examine the operational laws of Einstein t-norms under the IVFHFSs to further extend the study of the operational laws. Then, we introduce the interval-valued Fermatean hesitant fuzzy Einstein Bonferroni mean operator and the interval-valued Fermatean hesitant fuzzy Einstein weighted Bonferroni mean operator under Einstein t-norms. Our suggested aggregation operators consider the relationship between attributes and are far more flexible in comparison to the current approaches. Later, a novel MAGDM method based on Einstein Bonferroni operators under the IVFHFSs is given. Finally, the practicality and validity of the proposed method are demonstrated by a cardiovascular disease diagnosis application.

A trust relationship network-based consensus model in large-scale TODIM group decision-making

PurposeIn large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.Design/methodology/approachThis method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.FindingsThe proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.Originality/valueThis study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Consensus adjustment for multi-attribute group decision making based on cross-allocation

Consensus adjustment is crucial in group decision making (GDM), aiming to reduce discrepancies in decision makers’ (DMs’) judgments for final decision results. The consensus adjustments made by DMs are interactive rather than independent. However, few studies investigate the consensus adjustment problem from the perspective of interactions among DMs. This study first constructs a model to determine the total minimum consensus adjustment under the requirements of consensus threshold and individual-collective judgment bias. Then, a consensus adjustment approach based on cross-allocation scheme is proposed to address the inconsistency between individual and collective minimum adjustments, which concerns all DMs’ individual favorable allocation results under the total minimum consensus adjustment. The cross-allocation scheme ensures unanimous support for the same allocation result among all rational DMs, thereby eliminating the discrepancies among them. Subsequently, the optimization model-based minimum consensus judgment penalty is formulated to handle the non-cooperative behaviors of DMs. Furthermore, we show how to determine the unique consensus adjustment result for the non-cooperative DMs using the cross-allocation scheme. Therefore, our study contributes by developing a new GDM method that conducts consensus adjustment allocation by considering interactions among DMs. Finally, a case study is employed to demonstrate the specific application of the new method and discuss its efficiency. Meanwhile, comparison analysis and simulation experiments are provided.

Group decision making with incomplete triangular fuzzy multiplicative preference relations for evaluating third-party reverse logistics providers

The strategic management of reverse logistics (RL) is essential for enterprises to enhance their operational efficiency, customer satisfaction, and sustainability performance in today's competitive marketplace. Many manufacturing firms have to cooperate with professional RL providers to overcome resource constraints and technological limitations, ultimately driving business success. Thus, it is critical for every enterprise to select the most suitable third-party RL provider (3PRLP). This paper provides a novel group decision making (GDM) method with incomplete triangular fuzzy multiplicative preference relations (TFMPRs) to cope with the selection of the most optimal 3PRLP. Firstly, a definition of acceptable incomplete TFMPRs is given. Then, the sufficient and necessary condition of an acceptable incomplete TFMPR is proposed. By analyzing the properties of consistent TFMPRs, a graph-based algorithm is designed to estimate the unknown elements in incomplete TFMPRs. Based on the proposed acceptable consistency definition of TFMPRs, an optimization model is set up to improve the consistency degree of inconsistent TFMPRs. The optimal normalized triangular fuzzy multiplicative weight vector (Tri-MWV) is obtained by computing two analytic expressions and solving a linear programming model. To measure the closeness degree of two TFMPRs, the concept of logarithmic correlation coefficient (LCC) between two TFMPRs is proposed. Combining the incomplete preference information in incomplete TFMPRs with the LCCs of any two TFMPRs, an algorithm of computing experts’ weights is displayed. Subsequently, a novel method of GDM with incomplete TFMPRs is presented. Lastly, a practical example of evaluating 3PRLPs is conducted to illustrate the effectiveness of the proposed GDM method with incomplete TFMPRs.

Fair group decisions via non-deterministic proportional consensus

AbstractAre there group decision methods which (i) give everyone, including minorities, an equal share of effective decision power even when voters act strategically, (ii) promote consensus and equality, rather than polarization and inequality, and (iii) do not favour the status quo or rely too much on chance? We describe two non-deterministic group decision methods that meet these criteria, one based on automatic bargaining over lotteries, the other on conditional commitments to approve compromise options. Using theoretical analysis, agent-based simulations and a behavioral experiment, we show that these methods prevent majorities from consistently suppressing minorities, which can happen in deterministic methods, and keeps proponents of the status quo from blocking decisions, as in other consensus-based approaches. Our simulations show that these methods achieve aggregate welfare comparable to common voting methods, while employing chance judiciously, and that the welfare costs of fairness and consensus are small compared to the inequality costs of majoritarianism. In an incentivized experiment with naive participants, we find that a sizable fraction of participants prefers to use a non-deterministic voting method over Plurality Voting to allocate monetary resources. However, this depends critically on their position within the group. Those in the majority show a strong preference for majoritarian voting methods.

A quality function deployment model by social network and group decision making: Application to product design of e-commerce platforms

Quality function deployment (QFD) is an effective method to convert customer requirements (CRs) into design requirements (DRs) by constructing house of quality (HOQ). With the rapid growth of the e-commerce market, it is a new challenge to utilize the available online reviews to facilitate the implementation of QFD. Therefore, this paper proposes a novel QFD model from the perspective of group decision making (GDM) and social network analysis (SNA), then applies the proposed model to product design under Chinese e-commerce scene. Firstly, this paper extracts CRs from online reviews on e-commerce platforms, and the initial HOQs can be constructed. Then a bilateral negotiation GDM method based on SNA is carried out to generate a consensus-based HOQ, and therefore the final priorities of DRs can be obtained. Finally, a case study is provided to illustrate the applicability, and some discussions and comparative analysis are also conducted. The result indicates that the proposed method can generate effective and stable results for QFD implementation in real-world e-commerce scenario.

A group decision-making method for technology selection of petrochemical enterprises

Selecting appropriate energy conservation and emission reduction technologies is essential for the sustainable developments of petrochemical enterprises. It usually involves experienced experts from multi-domain and thus can be regarded as a large-scale group decision-making problem. Existing methods assumed that different experts use the same linguistic term set following the same semantics to express their evaluation values, and derived the ranking of alternatives by an indirectly obtained group preference matrix. Such an approach may induce information loss. To avoid these defects, this study introduces a model based on rank centrality and expert differences to choose appropriate technologies for petrochemical enterprises. First, an attitude parameter is introduced into a linguistic scale function to unify different judgment benchmarks of experts regarding the same linguistic term. Next, a direct weight elicitation method based on rank centrality is developed to deduce the individual rank vector of alternatives. A grey clustering method based on the similarities of individual rank vectors is extended to cluster experts into small groups, and the silhouette coefficient is used to determine clusters weights. Afterwards, the rank vectors of alternatives corresponding to subgroups and the global rank vector are calculated by the weighted averaging operator. Alternatives are ranked by the rank vector of the global group. Finally, four technologies were evaluated by the model, and the preferred technology was identified. The findings indicate that the proposed technology selection model alleviates information loss, elevates group consensus level, and provides a perspective for petrochemical enterprises in search of energy conservation and emission reduction technologies.

Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction

To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results.

A novel interval type-2 fuzzy consensus reaching process model and group decision-making method for renewable energy investment

Renewable Energy Investment (REI) is a critical challenge for both governments and investors, often complicated by conflicting criteria and uncertainties that must be managed to obtain reliable and useful decisions. To address the REI problems, a novel group decision-making approach under uncertainty is proposed. Our approach introduces the Interval Type-2 Fuzzy Sets (IT2FS) to construct linguistic terms that reflect their uncertainty and personalized semantics. Subsequently, we develop a novel automatic interval type-2 fuzzy Consensus Reaching Process (CRP) model to detect disagreements among experts and generate personalized recommendations to improve the consensus level. The proposed CRP model conducts interval analysis to facilitate consensus adjustments, thereby enhancing its capacity to describe uncertainty. Once the CRP is finished, the prospect theory is extended to the IT2FS to calculate the overall values for each alternative, which can capture the decision makers’ attitudes towards risk and uncertainty. Given that prospect values are characterized by interval values, we propose a two-stage interval-valued ranking approach to compare and rank them. Finally, an illustrative case study is provided to demonstrate the feasibility of our approach, along with comparative analysis to highlight its advantages and sensitivity analysis to show the robustness of parameters on decision results.

Group decision making based on linguistic interval-valued Atanassov intuitionistic fuzzy Yager weighted arithmetic aggregation operator of linguistic interval-valued Atanassov intuitionistic fuzzy numbers

In this paper, we propose a noval addition operation (AOP) and a novel scalar multiplication operation (SMOP) of linguistic interval-valued Atanassov intuitionistic fuzzy numbers (LIVAIFNs) based on Yager’s t-conorm and t-norm. The proposed AOP and the proposed SMOP of LIVAIFNs can conquer the shortcomings of the existing AOP and the existing SMOP of LIVAIFNs. Based on the proposed AOP and the proposed SMOP of LIVAIFNs, we propose the linguistic interval-valued Atanassov intuitionistic fuzzy Yager weighted arithmetic (LIVAIYFWA) aggregation operator (AO) of LIVAIFNs. We also prove some properties of the proposed LIVAIFYWA AO of LIVAIFNs. Moreover, by using the proposed LIVAIFYWA AO of LIVAIFNs, we propose a noval group decision making (GDM) method. The proposed GDM method can conquer the drawbacks of the existing GDM methods in the context of LIVAIFNs.

A Variable-Weight Model for Evaluating the Technical Condition of Urban Viaducts

Urban viaducts play a crucial role in transportation infrastructure and are closely linked to urban resilience. Accurate evaluation of their structural technical condition forms the basis for the scientific maintenance of urban viaducts. Currently, there is a lack of technical condition evaluation specifications for viaducts in China, and the existing bridge specifications that are similar do not fully align with the facility composition characteristics and maintenance management needs of viaducts. Therefore, this paper presents a technical condition assessment model for viaducts, based on existing bridge specifications. Considering the frequent damage to ancillary facilities of viaducts, the utilization of maintenance resources, and the impact on traffic operations, the model proposed in this paper adopts the Analytic Hierarchy Process (AHP) to introduce a new indicator layer for ancillary facilities. Subsequently, the weight values and deduction values of each layer of the model, as well as the findings of damage recorded in the new components, were determined using the Group Decision-Making (GDM) method and the Delphi method. This process forms a constant-weight evaluation model for assessing the technical condition of viaducts. Finally, to account for the impacts of significant damage to low-weight components on the structural condition, the variable-weight method was adopted to establish a comprehensive evaluation model with variable weights, which was then validated using practical viaduct examples. The results indicate that the variable-weight model provides a more accurate representation of the technical condition of viaducts, especially when components are severely damaged. Furthermore, this study examines the suitable conditions for implementing the constant-weight evaluation model and the variable-weight evaluation model, demonstrating that the variable-weight model is recommended when there is a significant disparity in the scores among the viaduct components, whereas the constant-weight model is applicable in other scenarios.