Weights Of Experts Research Articles

A multi-objective decision method for the network security situation grade assessment under multi-source information

Grade assessment of network security situation is summarized as a typical multi-index grade assessment problem. However, the existed methods for grade assessment of network security situation do not consider multi-source information such as the trust information among experts, the preference information among companies and heterogeneous information of companies. The above problems are unable to be solved through traditional assessment methods. The aim of this paper is to establish a novel multi-objective decision model for the grade assessment of network security situation under multi-source information. On the basis of describing the grade assessment problem of network security situation, the membership functions of four-type thresholds for the grades on attribute eigenvalue are put forward. Two trust information uncertainty degrees in social network are defined, and the trust transfer operator based on trust information uncertainty degree and multi-path trust integration method based on the variable weight function is proposed. Afterwards, a new method to generate the incomplete social network is used to identify the weights of experts. Then, a multi-objective decision grade assessment optimization model is further established to obtain the network security situation grades and grade discrimination based on the two-tuple linguistic operator. The proposed method provides a theoretical basis for constructing and testing the grade assessment of network security situation. Meanwhile, it develops the grade assessment system of advanced network security situation and improves the ability to protect network security.

Multi-attribute decision-making based on data mining under a dynamic hybrid trust network

The issues arising from the evolution of big data and network environment have rendered the relationship among experts to no longer be an independent multi-attribute group decision-making process. In response, a PROMETHEE multi-attribute decision-making method based on data mining under dynamic hybrid trust network is proposed. This method incorporates evaluation similarity based on degree centrality and expert trust values based on K-hop centrality in the social network to determine the experts’ weights. Furthermore, because the expert weight is somewhat affected by the attribute weight, the public-level attribute weight is obtained through data crawling and TF-IDF technology. The comprehensive attribute weight is acquired through a combination of expert-level attribute weights. Additionally, establishing a minimum adjustment cost model enables experts to follow a dynamic consensus reaching process in a composed hybrid trust network. Finally, to express the evaluation information more accurately in a complex linguistic environment, the probabilistic linguistic PROMETHEE method is introduced and applied to the site selection evaluation of charging and swapping stations. This highlights the feasibility and effectiveness of the proposed method through the comparison of decision-making methods and parameter sensitivity analysis.

An extended MEREC-EDAS approach with linguistic pythagorean fuzzy set for selecting virtual team members

Virtual teams (VT) have become increasingly popular due to modern technology. VT allows talented people from different places with different skills to work towards a common goal through network media. In order to form a more versatile VT, selecting VT members becomes a critical step. Based on the linguistic Pythagorean fuzzy sets (LPFS), this paper proposes an integrated approach to select VT members by means of the method based on standard removal effects (MEREC) and the method based on the mean solution distance of direct and indirect uncertainty (DIUEDAS). Firstly, decision information is described by LPFS. Secondly, MEREC is used to determine the criteria weights. Finally, the decision-making and evaluation laboratory (DEMATEL), failure mode and effects analysis (FMEA), and EDAS are combined to select the optimal VT members under the premise of evaluating the uncertainty in selecting VT members. In addition, this paper proposes a new method for determining expert weights. At the end of the paper, the model and the expert weight determination method are applied to the case of a port selecting VT members, and the effectiveness of the model proposed is demonstrated by comparative analysis in this paper.

A novel group multi-criteria sorting approach integrating social network analysis for ability assessment of health rumor-refutation accounts

Blooming social media platforms provide breeding ground for health rumors. Despite the establishment of accounts by numerous organizations to counter health rumors, the effectiveness of these endeavors exhibits considerable variability. Thus, there exists a pressing need to refine the framework and operation of rumor-refutation accounts. Aiming at enhancing the proficiency of accounts in refuting health rumors on social media platforms and exploring the factors affecting it, this paper proposes a novel group multi-criteria sorting approach integrating social network analysis (SNA) to classify accounts’ health rumor-refutation ability. To commence, an evaluation indicator system for accounts’ health rumor-refutation ability is established using SNA. Subsequently, the indicator values are computed, incorporating methods such as triangular fuzzy number (TFN), a lite bert (ALBERT) pre-trained language model, and PageRank. Furthermore, hesitant fuzzy linguistic term set (HFLTS) and triangular intuitionistic fuzzy number (TIFN) are used to determine the expert weights and indicator weights. After that, on the basis of original best worst method-sort (BWM-Sort), classification boundaries are discovered creatively using optimal clustering (OC), and minimum discrimination information (MDI) is adopted as the objective function for priority assignment. Consequently, an OC-MDI-BWM-Sort method is newly proposed which offers distinct advantages in computational efficiency, information integration, decision-making objectivity, and result effectiveness. Lastly, regarding to four cases of widely circulated rumors, health rumor-refutation ability of 35 accounts on Weibo platform is classified using the proposed method. The findings underscore that merely 8.57% of accounts exhibit stable and good health rumor-refutation ability, while up to 28.57% and 80.00% display poor and inconsistent ability in certain instances. Tailored to accounts with excellent or good, satisfactory or fair, and poor health rumor-refutation ability, respectively, managerial suggestions are provided regarding information expression standards, account operator proficiency, and account cooperation, and all accounts are advised to watch audience behavior.

The challenges of ideological and political education in universities based on the Internet environment and its optimization path

Abstract In order to explore the challenges and optimization paths faced by ideological and political education in colleges and universities under the Internet environment. This paper establishes a comprehensive evaluation model for the ranking of university online public opinion, conducts quantitative posture analysis based on rank affiliation function, starts qualitative posture analysis based on language grade evaluation, determines objective weights of quantitative and qualitative posture ranking indicators by using deviation method, uses binary, ordered ratio method to determine for expert weights, and integrates subjective and objective assignments based on empirical factors. After establishing the comprehensive evaluation of the posture level of college network public opinion, the influence of network public opinion on thinking and political education was analyzed: bad network information can easily distort college students’ ideology and value orientation, and the posture level evaluation of the influence of network public opinion on college students’ values reached −0.85. Meanwhile, the network public opinion hinders the formation of the moral sentiment of college students, and the posture level evaluation of the influence reached −0.67. This study analyzes the influence of college network public opinion on thinking and politics from both qualitative and quantitative aspects and provides an intentional reference for college thinking and politics education to respond effectively to the network environment.

A social network analysis-based model for failure mode and effect analysis under linguistic preference relation environment

Failure mode and effect analysis (FMEA) is a proactive risk analysis technique widely used to improve the reliability and safety of complex systems in various industries. Generally, FMEA is a multidisciplinary team activity in which the trust relationships among experts have an influence on the risk assessment process and the risk priority of failure items. Furthermore, it is significant to obtain the risk ranking result of failure items with a high consistency degree in the group FMEA. Therefore, in this study, we aim to develop a new FMEA model based on double hierarchy hesitant linguistic preference relations to derive the risk priority of failure items considering experts' social network. First, the risk assessments of experts are described by using the double hierarchy hesitant linguistic preference relations via pairwise comparisons of failure items. Second, the relative weights of FMEA experts are derived based on their trust relationships in social network and the consistency of failure risk evaluations. Third, consistency checking and repairing algorithm are performed to address experts’ self-contradictory opinions, and the risk priority of failure items is determined in line with their risk degrees. Finally, a practical risk evaluation case is provided to demonstrate the applicability of the proposed FMEA model, and a comparative analysis is conducted to illustrate its effectiveness and advantages.

A consensus method in social network large-scale group decision making with interval information

Large-scale group decision making (LSGDM) is considered when the number of experts involved in the decision exceeds 20. Due to the large number of people involved in LSGDM, its decision-making process is uncertain, complex, and time-consuming. Therefore, how to effectively help large groups reach consensus in a complex environment is a challenge for current research. Consensus reaching process (CRP) is an effective tool to eliminate group conflicts. Based on this, we propose a consensus reaching process based on the Louvain algorithm, social network, and bounded confidence (SNBC) model with interval numbers. First, we use interval numbers to express expert opinions and social network relationships among experts. Second, the experts are clustered using the Louvain algorithm. The weights of experts are obtained by social network analysis. Third, we use the SNBC model to design a feedback mechanism for tripartite opinions. In addition, we give a numerical example and simulation experiments to demonstrate the flexibility and effectiveness of the proposed approach. Finally, the comparative analysis shows the superiority of our method.

Interval-valued Pythagorean fuzzy operational competitiveness rating model for assessing the metaverse integration options of sharing economy in transportation sector

The rise of the sharing economy (SE) has enabled new forms of urban mobility and had a substantial impact on the transportation sector. The applications of SE transportation lessen vehicle ownership and single-occupancy vehicles, which contributes to the sustainability of a region. Metaverse is a digital ecosystem that integrates SE applications with transportation sector and improves the sustainability of SE applications. The aim of this work is to assess and prioritize the Metaverse integration options of sharing economy from multiple criteria and uncertainty perspectives. In this regard, the proposed study develops a four stage decision-making method on interval-valued Pythagorean fuzzy (IVPF) setting. First, new score function is proposed to compare the interval-valued Pythagorean fuzzy numbers (IVPFNs) and further used to derive the weights of decision experts in the proposed method. Second, some IVPF interaction aggregation operators are proposed to aggregate the individual’s information in group decision-making. Third, an integrated weighting process is presented to obtain the objective weights of criteria using cross entropy-based formula and subjective weights by relative closeness coefficient-based model with IVPF information. For this purpose, a cross entropy is introduced for interval-valued Pythagorean fuzzy sets. Fourth, a hybrid operational competitiveness ratings assessment (OCRA) model is developed to rank the alternatives with IVPF information. Moreover, a case study of Metaverse integration options of sharing economy in transportation sector is taken to express the practicability and effectiveness of the presented method. Sensitivity analysis with respect to various values of parameter is discussed to reveal the stability of outcomes. It is also compared with some extant models to validate the superiority of developed method.

Energy based bipolar intuitionistic fuzzy digraph decision-making system in selecting COVID-19 vaccines

The energy of a graph, defined as the absolute sum of the eigenvalues (EIGs) of its adjacency matrix, has been applied to numerous research fields, including decision-making techniques. It can be defined for a fuzzy graph when the graph has uncertain vertices and edges. A bipolar intuitionistic fuzzy graph (BIFG) is an extended version of fuzzy graph that helps to deal with uncertainty in an effective manner. Therefore, this study unites the above-said concepts to examine the energy concepts with additional information and utilizes them in the decision-making problem to explore their applicability. Initially, the notions, relations and bounds of the energy, Laplacian energy (LE) and signless Laplacian energy (SLE) are defined. Further, a novel energy-based multi-criteria decision-making (MCDM) technique is designed by utilizing the energy of BIFG to obtain the objective weights for the decision experts. The selection problem of five COVID-19 vaccines with respect to eight important criteria is taken to elucidate the proposed decision-making technique, where Moderna has resulted as the finest vaccine among other vaccines. Moreover, a comparative is done with the correlation and distance measures to show the superiority of the proposed method. Finally, a sensitivity analysis is done to depict the reliability of the proposed technique.

A two-phase multi-criteria decision-making with interval-valued Pythagorean normal fuzzy information and its application for module hospital site selection in combating COVID-19

During the fight against COVID-19, module hospital saved tens of thousands of lives and families. Therefore, how to effectively select the site of module hospital is a critical issue. The site selection of module hospital is a multi-criteria decision-making (MCDM) problem. Recently, interval-valued Pythagorean normal fuzzy (IPNF) number has become an effective and powerful tool for handling ambiguity in decision-making. However, the weights of experts in existing IPNF decision-making are often directly given, lacking objectivity and persuasiveness. Moreover, the current research on aggregation operators in IPNFN only involves simple weighted average and geometry, while ignoring the interaction among fuzzy information. Motivated by these issues, this paper aims to develop a two-phase IPNF MCDM approach and apply it to the site selection of module hospital. Firstly, IPNF Einstein weighted averaging (IPNFEWA) operator, IPNF Einstein order weighted averaging (IPNFEOWA) operator, IPNF Einstein weighted geometric (IPNFEWG) operator, and IPNF Einstein order weighted geometric (IPNFEOWG) operator are proposed. Moreover, numerical and theoretical proofs of the proposed operators are carried out. Secondly, an algorithm for determining experts’ weights is developed using the proposed IPNF social network in the first phase, and the decision-making process is conducted in the second phase. Finally, a numerical example on the site selection of module hospital is provided to demonstrate the feasibility of the proposed method. The numerical results of comparisons and sensitivity analysis are further analyzed to confirm the advantages of the proposed method.

An investment decision framework for offshore wind-solar-seawater pumped storage power project under interval-valued Pythagorean fuzzy environment

Offshore wind-solar-seawater pumped storage (wind-PV-SPS) power system will be a very competitive offshore new energy project in the future because it can realize the complementarities of wind and photovoltaic resources in the dimensions of time and space, and reduce the waste of resources caused by voltage instability. In order to promote the sustainable development of offshore wind-PV-SPS system, it is particularly important to construct a scientific and reasonable investment decision-making framework. Firstly, a comprehensive criteria system is established in this paper, which includes economy, resource, environment, supporting conditions and market. Secondly, the interval-valued Pythagorean fuzzy set (IVPFS) is introduced to describe the uncertainty and fuzziness of information in the process of investment decision, and the expert weight correction model is established based on the similarity of IVPFSs. At the same time, IVPF-DEMATEL method is utilized to determine the weight of criteria, which takes the interaction between criteria into account. In addition, considering the decision-maker's risk preference psychology, we introduce the GOWUA-HARA operator into the PROMETHEE II method, and extend it to the interval-valued Pythagorean fuzzy environment to choose the optimal investment alternative. Finally, the proposed framework is applied to a case study. Then sensitivity analysis and comparative analysis are carried out to verify its feasibility and robustness.

Group decision making based on relative projection between fuzzy preference relations

Group decision making (GDM) has been widely focused on for coping with the complexity of a practical decision making problem. This paper proposes a relative projection-driven GDM model with fuzzy preference relations (FPRs). First, a novel additive consistency index of FPRs is constructed using the variance of relative projections between column/row vectors in two FPRs, which is called the relative projection variance consistency index (RPVCI). The thresholds of RPVCI for FPRs with acceptable additive consistency (AAC) are calculated using the simulation method. For improving consistency of FPRs, a convergent iteration algorithm is constructed by equipping subjective and objective adjustment parameters. Second, the group consensus level is captured using relative projections of individual FPRs on the collective one. An iteration model is reported to optimize the consensus degree by combining the weights of experts with a comprehensive adjustment coefficient that depends on both subjective and objective factors. Third, a novel GDM algorithm is proposed by integrating the two proposed iteration models. Comparisons with some existing models are offered by carrying out a case study to show the novelty and advantage of the proposed GDM model.

A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments.

A novel consensus reaching method for the preference-approval structure based on regret theory and its application in evaluating pension institutions

PurposeThe purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.Design/methodology/approachFirst, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.FindingsThe feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.Research limitations/implicationsThis paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.Originality/valueA new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

Motion In-Betweening with Phase Manifolds

This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights. Each generated set of weights then produces a sequence of poses in an autoregressive manner between the current and target state of the character. In addition, to satisfy poses which are manually modified by the animators or where certain end effectors serve as constraints to be reached by the animation, a learned bi-directional control scheme is implemented to satisfy such constraints. The results demonstrate that using phases for motion in-betweening tasks sharpen the interpolated movements, and furthermore stabilizes the learning process. Moreover, using phases for motion in-betweening tasks can also synthesize more challenging movements beyond locomotion behaviors. Additionally, style control is enabled between given target keyframes. Our proposed framework can compete with popular state-of-the-art methods for motion in-betweening in terms of motion quality and generalization, especially in the existence of long transition durations. Our framework contributes to faster prototyping workflows for creating animated character sequences, which is of enormous interest for the game and film industry.

Integrated FAHP-FDEMATEL for determining causal relationships in construction crew productivity modelling

Construction crew productivity is affected by the motivation of the crew performing given activities and by situational/contextual factors forming the dynamic construction environment. The literature lacks a comprehensive analysis of causal relationships between crew motivation and situational/contextual factors for dynamic modelling of crew productivity. The contributions of this paper are (1) identifying a set of criteria for performing expert weight assignment for heterogenous group experts in productivity research, (2) proposing an integrated fuzzy analytic hierarchy process–fuzzy decision-making trial and evaluation laboratory approach that provides a systematic, structured method for determining causal relationship mapping between factors affecting crew productivity, and (3) proposing an approach for identifying cause-and-effect groups among the situational/contextual factors and crew motivation, which can further be used to formulate strategic productivity improvement solutions. The proposed methodology is demonstrated using a case study on an actual industrial construction project in Alberta, Canada.

A trust-based quantum probabilistic linguistic multi-criteria group decision making model considering interference effect

In group decision-making (GDM), there exist cognitive and realistic uncertainties and humans exhibit hesitation and ambiguity. Using probabilistic linguistic term sets (PLTSs) can better adapt to the above challenges. However, the current PLTS-based methods rarely take the subjectivity and behavior of experts into account, especially the influence of trust relations and subjective beliefs. This paper combines the trust network and the quantum probability theory (QPT) in GDM, and proposes a new probabilistic linguistic multi-criteria group decision-making (MCGDM) model considering the trust relationships and the interference effects between experts. Firstly, probabilistic linguistic evaluations and trust information are collected. Then, the entropy-weight method based on PLTS is proposed to determine criterion weights. A new mixed multi-path trust transfer aggregation operator that integrates both the path length and the path quality is defined, to establish a complete trust network and measure the expert weight. Thirdly, because of the certain interference between experts, a quantum-like Bayesian network framework is built to aggregate the opinions of experts. During the process, the system is in a superposition state, and personal opinions are regarded as interfering wave functions, affecting the final results. Finally, an example of the prevention and control of COVID-19 in a city is used for verification and sensitivity and comparison analyses are provided to verify the effectiveness of the method.

Reducing inconsistencies of FAHP in structural safety assessment of diversion tunnels

The Fuzzy Analytic Hierarchy Process (FAHP) is an effective method for assessing risks and has great potential for assessing the structural safety of diversion tunnels. However, this type of professional assessment can become more complicated due to the presence of multiple factors that influence the safety of the tunnel. This complexity not only necessitates the establishment of a comprehensive index system but also introduces uncertainties related to inconsistencies in expert judgments and unknown expert weights in group decision-making process. To address these issues, this study proposed an improved FAHP method for the structural safety assessment of diversion tunnels. This method incorporates a proposed comprehensive index system and incorporates a preceding ordering process before pairwise comparisons in the questionnaire to enhance the consistency in expert judgment. The consistency and compatibility of expert judgment matrices are quantified using a distance vector rather than a single number, and a reasonable comprehensive weight is calculated based on the Dempster–Shafer (D–S) evidence theory. We applied this method to the structure safety assessment of the Houziyan hydropower station diversion tunnel and found an overall low risk for the tunnel, but significant issues were identified regarding lining crack. Furthermore, we conducted a comparative evaluation of the proposed method against other related methods, thereby demonstrating its high quality. The study contributes an improved FAHP method for the structural safety analysis of diversion tunnels and encourages the wider application of FAHP in more complicated systems.

Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences

With a growing focus from the United Nations to eradicate the ill effects of climate change, countries around the world are transforming to green and sustainable habits/practices. Adoption of clean energy for demand satisfaction is a prime focus of many countries as it reduces carbon trace and promotes global development. In developing countries like India, there is an urge for sustainable global development. Literature shows that direct and complete adoption of clean energy incurs some barriers, which impede the sustainable development of the nation. Grading such barriers supports policymakers to effectively plan strategies, which motivates authors to put forward a novel decision model with integrated approaches. First, qualitative rating data on barriers and circular economy (CE) factors are collected from experts via questionnaires, which are transformed into q-rung orthopair fuzzy information (qRFI). Second, the weights of experts and CE factors are determined by the proposed variance measure and CRITIC. Third, barriers are graded by the proposed ranking algorithm that considers modified WAPAS formulation. Finally, these approaches are integrated into a model that is testified for practicality by using a case example from India. Sensitivity and comparative analyses are performed to realize the merits and limitations of the model for extant works.

A Weighting Method Based on the Improved Hesitation of Pythagorean Fuzzy Sets

The existing expert weight determination method for multi-attribute decision making based on the Pythagorean fuzzy number approach does not make sufficient use of the hesitation involved with the decision information, which may cause biased weight assignment. Therefore, to address the issue of unknown expert weights and attribute evaluation based on Pythagorean fuzzy numbers in multi-attribute group decision-making problems, a weight determination method is proposed that improves the treatment of hesitation in Pythagorean fuzzy sets. Firstly, the proximity of experts and similarity of the modified ones are determined according to the evaluation matrix. Then, the expert weights are integrated from the aspects of proximity and corrected similarity to obtain an assembled comprehensive evaluation matrix. Finally, the alternatives are ranked using the PF-TOPSIS method. The results of expert weight analysis and data verification demonstrate that the proposed method fully utilizes expert decision-making information, leading to a significant improvement in the rationality and accuracy of multi-attribute group decision-making problems.