Weights Of Experts Research Articles

An advanced decision support framework to assess sustainable transport projects using a new uncertainty modeling tool: Interval-valued Pythagorean trapezoidal fuzzy numbers

This paper introduces a new decision support framework for transport projects appraisal, which is based on the multi-criteria decision analysis (MCDA) and the cost-benefit analysis (CBA) to address both monetary and non-monetary characteristics of the problem at the same time. Substantial inaccuracies are present while obtaining non-monetary and monetary inputs to the MCDA and the CBA, respectively. To provide the MCDA part of the framework with uncertainty, an interval-valued Pythagorean fuzzy set (IVPFS) is used as an attractive uncertain modeling tool. Additionally, a new multi-attribute group decision-making (MAGDM) method with partially known attribute weights and unknown expert weights under the IVPFS environment is proposed to find the best sustainable transport project from the non-monetary criteria point of view. Furthermore, a novel uncertainty modeling tool, namely an interval-valued Pythagorean trapezoidal fuzzy set, is introduced to address ill-known quantities and inaccuracies that exist in the estimation of monetary impacts. Finally, this framework is used to handle a real case study of a complex decision process of finding the best transport project to fund several candidate projects. This approach has made it possible to handle the decision process from economic, social, environmental points of view at the same time. The result of this case study shows that the decision-making framework can be used as a valuable decision support tool. Moreover, it is shown that the presented framework can be applied to effectively support appraisals of transport projects.

Dual hesitant fuzzy decision making in optimization models

Considering the advantages of dual hesitant fuzzy elements (DHFEs) for describing the hesitant and intuitionistic judgments of experts and identifying the limitations of previous research about dual hesitant fuzzy decision making, this paper studies decision making with dual hesitant fuzzy preference relations (DHFPRs) and provides a new group decision making (GDM) method based on a series of built optimization models. A multiplicative consistency concept for DHFPRs is first introduced and analyzed. On the basis of this concept, optimization describing the multiplicative consistency of DHFPRs is realized. Meanwhile, stepwise optimization models for obtaining complete multiplicatively consistent DHFPRs are constructed, and a method for calculating the intuitionistic fuzzy priority vector is offered. Then, optimization models for determining unknown values in incomplete DHFPRs are formed in view of the multiplicative consistency. An index for measuring the consensus is defined and optimization models for reaching the consensus requirement of a group are established. Furthermore, consensus-based optimization models for determining the weights of experts are established. In view of the multiplicative consistency and consensus analysis, a detailed procedure for GDM with incomplete and inconsistent DHFPRs is provided. Finally, the new method is illustrated by a case study concerning an evaluation of the environmental performance of enterprises and associated comparative analyses are reported.

Prospectivity modelling of Canadian magmatic Ni (±Cu ± Co ± PGE) sulphide mineral systems

New mineral deposit discoveries are required to meet the forecasted demand for some critical raw materials. Governments are responding to that challenge by investing in mineral systems research and by by making government geoscience datasets freely available to the public and explorers. However, translating conceptual mineral system models to mappable geological, geochemical, and geophysical proxies is difficult with incomplete data of variable quality from modern and legacy surveys. Herein we address those knowledge gaps and propose a new open source workflow in R for prospectivity modelling using public geoscience datasets. We focus on the largest footprints of magmatic Ni (±Cu) sulphide mineral systems and their critical raw materials (±Co ± PGE). Multiple prospectivity models are presented, including data-driven methods (e.g., weights of evidence, gradient boosting machines) that use the features of known mineral occurrences as a training set and a hybrid method that also incorporates conceptual mineral system criteria. All models are validated using data from northern Canada (i.e., north of 60° latitude) as a test set. Statistical analysis of the prospectivity results suggests that rock types and geological ages are two of the most important predictive datasets, which correspond to the sources and drivers within the mineral system framework, respectively. Variable importance plots further suggest that geological boundaries (e.g., horizontal gradient magnitude of the gravity data and multi–scale edges) and the close spatial association between areas of high mineral potential and the edges of thick continental crust represent prospective ore-forming pathways. Model performance and the best combination of predictors and hyperparameters for each model are based on the receiver operating characteristics (ROC) plots, which yield a range of area under the curve (AUC) from 0.846 to 0.923 for the spatially independent test set. Most Canadian geological provinces, possibly with the exception of the Grenville orogen for the hybrid and weights of evidence methods (AUC = 0.716–0.726), yield comparable model performance, suggesting that the heterogeneous spatial distribution of different mineral system sub-types (e.g., komatiite-associated, rift-related, Alaskan-type, and hydrothermal awaruite) have a relatively minor impact on the prospectivity results. Monte Carlo-type simulations further suggest that the expert weightings used in the hybrid method (AUC = 0.843) are only slightly better than an average model constructed from random combinations of weightings (AUC = 0.809). The general agreement between different methods and multiple iterations of the same model demonstrate that public geoscience datasets can effectively reduce the search space to support mineral exploration targeting (i.e., less than 8% of map pixels contain more than 80% of the known Ni mineralization). However, vast segments of the Canadian landmass have not undergone systematic geological surveying or data acquisition. Prospectivity modelling can thus also be used by governments and academia to prioritize areas for future targeted geoscience research.

Extended failure mode and effect analysis approach based on hesitant fuzzy linguistic Z-numbers for risk prioritisation of nuclear power equipment failures

The successful diagnosis of nuclear power equipment failures plays a vital role in guaranteeing the safe operation of nuclear power systems. Failure mode and effect analysis (FMEA) is one of the most commonly used methods for identifying potential failures. However, several shortcomings associated with the conventional FMEA method limit its further application. This paper develops an extended FMEA approach based on hesitant fuzzy linguistic Z-numbers (HFLZNs). Firstly, the concept of HFLZNs is proposed to describe the evaluation information, which inherits the prominent features of the hesitant fuzzy linguistic term set and linguistic Z-numbers (LZNs). Secondly, an HFLZN assessment method is developed to determine the weights of risk factors, and the weights of experts are measured based on hesitation degree. Subsequently, considering the psychological characteristics of decision makers, Tomada de Decisão Iterativa Multicritério and LZNs are integrated to obtain the risk ranking of failure modes. Finally, the practicability of the extended FMEA method is proven by an illustrative example concerning the risk evaluation of a nuclear main pump bearing, and its robustness is verified by indepth analysis.

Combination Evaluation Method of Fuzzy C-Mean Clustering Validity Based on Hybrid Weighted Strategy

Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy C-means (FCM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clustering validity evaluation method is verified by experiments on artificial data sets and UCI data sets. The experimental results show that the proposed fuzzy clustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.

Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information and their application in decision support system

Probabilistic hesitant fuzzy Set (PHFs) is the most powerful and comprehensive idea to support more complexity than developed fuzzy set (FS) frameworks. In this paper a novel and improved TOPSIS-based method for multi-criteria group decision making (MCGDM) is explained through the probabilistic hesitant fuzzy environment, in which the weights of both experts and criteria are completely unknown. Firstly, we discuss the concept of PHFs, score functions and the basic operating laws of PHFs. In fact, to compute the unknown weight information, the generalized distance measure for PHFs was defined based on the Probabilistic hesitant fuzzy entropy measure. Second, MCGDM will be presented with the PHF information-based decision-making process.

A New Enhanced ARAS Method for Critical Path Selection of Engineering Projects with Interval Type-2 Fuzzy Sets

Appraising the alternative among a set versus conflicting factors is taken into account as an essential issue in real-world decision problems. In this paper, a new multicriteria group decision-making (MCGDM) model is introduced with a fuzzy relative preference relation (FRPR) concept and additive ratio assessment (ARAS) method. FRPR is a useful way to reduce time complexity and to avoid pairwise comparisons. Also, a new method for determining the weights of experts by using an extension of the reference point method is presented and added to this new MCGDM model for an aggregation of the experts’ judgments. Finally, a case study about the selection problem of the engineering project in exploration and sampling projects of lead and zinc mine by decisive criteria are solved to denote the capability of the presented model. Furthermore, to illustrate the various applications of the proposed method in different decision problems, an example of literature is solved.

Emergency decision-making method based on multi-attribute intuitionistic fuzzy and evidence theory

Multi-attribute emergency decision-making problems have the characteristics of incomplete information and shortage of response time. Evidence theory can effectively express uncertain information in the decision-making process. However, evidence theory requires that the condition of evidence independence is met, and the evaluation information of experts is often vague. Therefore, an emergency decision-making method based on intuitionistic fuzzy sets and evidence theory is proposed. First, each expert gives an intuitionistic fuzzy evaluation of each emergency plan. Secondly, the proposed intuitionistic fuzzy similarity calculation method is used to obtain the similarity between experts and determine the expert weight. The attribute weight is known, the intuitionistic fuzzy evaluation is converted into a Mass function, and the evaluation of expert's decision-making plan is revised and fused using evidence theory to obtain the final decision. Finally, an example analysis proves that the model is feasible and effective.

Module partitioning study of specialized engineering services based on modular theory ——case on coal production firms

Outsourcing of specialized engineering services has become one of the routes to the innovation and high-quality development of the commercial model of coal production enterprises in China. Currently, although there is practice, no scale-up development has been achieved.In this paper, based on modular theory, under the condition of unknown expert weights, triangular fuzzy number method is used to construct a service unit association relationship evaluation model, and the correlation matrix is calculated. Then, a genetic algorithm is used to cluster the association matrix and obtain the results of the division of the outsourced module of engineering services. Finally, the proposed method is validated using the coal mine development process of coal producers as an example.

Life Cycle Consulting of Engineering Construction Risk Evaluation Based Intuitionistic Fuzzy Sets and Euclidean Similarity

This paper studies life cycle consulting of engineering construction risk. Firstly build a risk evaluation hierarchy for the life cycle consulting of engineering construction risk, and then transform expert evaluation information into intuitionistic fuzzy sets to form an evaluation matrix, use intuitionistic fuzzy entropy to describe the degree of fuzziness of expert information and determine the criterion weight; then use Euclidean distance to determine the similarity of expert information The expert weight is determined by combining the degree of hesitation with the degree of hesitation, and the score function is used to determine the comprehensive score value of each risk factor, and the risk factor is predicted accordingly.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems, where 2-dimension uncertain linguistic variables (2DULVs) are used as the reliable way for the experts to express their fuzzy subjective evaluation information. Firstly, in order to measure the 2DULVs more accurately, a new method is proposed to compare two 2DULVs, called a score function, while a new function is defined to measure the distance between two 2DULVs. Secondly, two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative. Then, a score function is used to determine the ranking of the alternatives. Finally, the effectiveness of the proposed method is proved by an illustrated example.

An Integrated Consensus Improving Strategy Based on PL-Wasserstein Distance and Its Application in the Evaluation of Network Public Opinion Emergencies

In real life, multiple network public opinion emergencies may break out in a certain place at the same time. So, it is necessary to invite emergency decision experts in multiple fields for timely evaluating the comprehensive crisis of the online public opinion, and then limited emergency resources can be utilized to give priority to respond to the one with the highest crisis. Due to the complexity of network public opinion emergencies and the limited cognition of experts, most of the decision problems for evaluating the network public opinion emergencies are highly uncertain. Also, prior to the selection of the highest crisis, it is preferable that experts reach a high degree of consensus among their assessments or opinions. To address such problems, this paper presents a novel adaptive consensus reaching model for multiattribute group decision making (MAGDM) with probabilistic linguistic decision matrices (PLDMs). First, to quantify the difference between any two probabilistic linguistic term sets (PLTSs) accurately and efficiently, we define a novel distance measure between PLTSs based on the Wasserstein metric. Then, by integrating the defined PLTSs-based Wasserstein (PL-Wasserstein) distance measure into the classical CCSD method, we construct an optimization model for objectively determining attribute weights. Subsequently, we develop the individual cumulative consensus contribution (ICCC) measure and the group consensus measure, respectively, following which is to present an integrated consensus improving strategy that considers both weight-updating (i.e., dynamic weights of experts and attributes) and assessment-adjusting. Finally, the feasibility and the applicability of the proposed approach are illustrated via a real evaluation of network public opinion emergencies. Through comparing with existing probabilistic linguistic MAGDM approaches, the proposed approach offers the advantages in terms of the accurate measurement of information difference and the integrated improvement of consensus efficiency.

Evaluating community question-answering websites using interval-valued intuitionistic fuzzy DANP and TODIM methods

As important knowledge-sharing platforms, community question-answering (CQA) websites have attracted the attention of many users. An evaluation of the quality of CQA websites can assist users in selecting high-quality CQA websites and can help the operators of CQA websites achieve the most beneficial improvements. An approach to the evaluation of CQA websites is proposed in this study. First, a CQA service quality model is constructed to evaluate the quality of CQA websites based on the E-SERVQUAL model. A novel multicriteria decision-making (MCDM) model is proposed to address the ratings. In the model, uncertainty and consistency are combined in an interval-valued intuitionistic fuzzy (IVIF) environment to determine two kinds of expert weights. The DANP method, along with experts’ weights, is extended to the IVIF environment to derive the criteria weights. The IVIF TODIM method, along with experts’ weights, is used to rank the CQA websites in terms of quality. Finally, the proposed approach is applied to evaluate the quality of five CQA websites. The experimental results of the application, along with sensitivity analysis and comparative analysis, demonstrate the feasibility and practicality of the proposed approach.

A soft computing approach based on critical chain for project planning and control in real-world applications with interval data

Resource-constrained project scheduling problem (RCPSP) has been one of the most important topics in project scheduling in recent decades. RCPSP, due to the strategic importance of the projects and internal and external pressures for timely completion, is a very challenging task. When executing a project, controlling and monitoring it also becomes vital. This paper aims to present a new soft computing framework that incorporates decision making about RCPSP parameters, RCPSP modeling, adding project and activities buffer, and monitoring the project. In the decision-making procedure, the activities durations are interval, but resource requirements are real numbers. So, the decision-making problem needs a hybrid procedure. To overcome this matter, the hybrid projection measure is extended to obtain the experts weights and build the aggregated decision matrix. In the RCPSP section, the activities durations are not determined and vary between certain ranges. The resource requirements and range of activities durations are obtained from group decision-making method. In addition, this model is solved with simulated annealing (SA) algorithm. In the third step, buffers are considered in a way that allocating project buffer to activities’ buffers becomes based on a new normalized important factor. The normalized important factor is introduced by considering activity duration and resource requirements. Finally, a novel controlling procedure is extended by activity buffer monitoring. Two buffer threshold sets and violations are applied, and each one of them sends a particular alarm to the project manager. Project manager’s decisions in optimistic and pessimistic situations are discussed. Ultimately, the method is solved in a real case study, and the results are discussed. The application shows that the presented method is flexible in many situations. It also increases the probability of timely completion of the project in addition to tracking the deviations from the plan. The proposed method introduces a comprehensive framework, so it gives project managers a better vision. It can also act as an ideal monitoring tool to control schedule deviations and to help project manager for proper actions during the project execution.

A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Adaptive consensus building in emergency group decision-making with hesitant fuzzy linguistic information: a perspective based on disappointment theory

In this article, an adaptive consensus model that considers individual disappointment emotion is proposed for emergency multi-attribute group decision-making (MAGDM) problems with hesitant fuzzy linguistic information. Subsequently, it is applied to choose the optimal emergency alternative(s) for the prevention and control of COVID-19 on a college campus. The main innovations and contributions of this article are as follows: (a) Individual modified perceived utility (MPU) based on disappointment theory is integrated into the determination of attribute weights and construction of consensus reaching process (CRP). (b) The MPU-based individual contribution degree and the MPU-based soft group consensus degree are developed. (c) The new emergency CRP mechanism not only considers the rewards and penalties of expert weights but also regards the adaptive updating of attribute weights. Compared with the existing emergency MAGDM models in a hesitant fuzzy linguistic environment, the proposed consensus model has some advantages in improving consensus efficiency and simulating uncertain psychological behavior.

An Interval-Valued Best–Worst Method with Normal Distribution for Multi-criteria Decision-Making

A novel three-stage multi-criteria decision-making (MCDM) method considering the interval-valued best–worst method (BWM) and social networks including the following three main modules is put forward: (1) the weights of experts; (2) the weights of criteria; (3) the weighted sum method (WSM). Firstly, a new framework for computing the weights of experts in social networks is construct. Let the values of trust and distrust be interval values rather than fixed values, and then construct a complete trust preference matrix through trust propagation. Subsequently, trust score obtained by synthesizing the trust preference matrix is used to assign the weights of experts. Secondly, an interval-valued BWM model with normal distribution is developed to compute the weights of criteria. The proposed BWM model innovatively uses interval fuzzy numbers with normal distribution to represent the preference degree of experts instead of the fixed values in the basic BWM model. Thirdly, considering the WSM, this paper constructs a novel three-stage MCDM method considering the proposed novel BWM model and social networks. The proposed MCDM model innovatively aims at the situation where both the weights of experts and criteria are not known, and provides mathematical models for solving them respectively. Finally, a case is used to prove the utility and availability of the novel MCDM method. The results show that the novel MCDM method can not only reduce the subjectivity in decision making, but also can effectively solve the MCDM problems.

Project Portfolio Resource Risk Assessment considering Project Interdependency by the Fuzzy Bayesian Network

Resource risk caused by specific resource sharing or competition among projects due to resource constraints is a major issue in project portfolio management, which challenges the application of risk analysis methods effectively. This paper presents a methodology by using a fuzzy Bayesian network to assess the project portfolio resource risk, determine critical resource risk factors, and propose risk-reduction strategies. In this method, the project portfolio resource risk factors are first identified by taking project interdependency into consideration, and then the Bayesian network model is developed to analyze the risk level of the identified risk factors in which expert judgments and fuzzy set theory are integrated to determine the probabilities of all risk factors to deal with incomplete risk data and information. To reduce the subjectivity of expert judgments, the expert weights are determined by combining experts’ background and reliability degree of expert judgments. A numerical analysis is used to demonstrate the application of the proposed methodology. The results show that project portfolio resource risks can be analyzed effectively and efficiently. Furthermore, “poor communication and cooperation among projects,” “capital difficulty,” and “lack of sharing technology among projects” are considered the leading factors of the project portfolio resource risk. Risk-reduction strategic decisions based on the results of risk assessment can be made, which provide project managers with a useful method or tool to manage project risks.

Failure mode and effect analysis using multi‐linguistic terms and Dempster–Shafer evidence theory

AbstractFailure mode and effect analysis (FMEA) is a useful technique to identify and quantify potential failures. FMEA determines a potential failure mode by evaluating risk factors. In recent years, there are many works improving FMEA by allowing multiple experts to use linguistic term sets to evaluate risk factors. However, it is important to design a framework that can consider both the weight of risk factors and the weight of the experts. In addition, managing conflicts among experts is also an urgent problem to be addressed. In this paper, we proposed an FMEA model based on multi‐granularity linguistic terms and the Dempster–Shafer evidence theory. On the other hand, the weights for both experts and risk factors are taken into consideration. The weights are computed objectively and subjectively to ensure the reasonability. Further, we apply our method to an emergency department case, which shows the effectiveness of the method.

Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach

In the era of circular economies, governments and consumers are increasingly aware of environmental protection, which encourages enterprises to devote more attention to reverse logistics (RL). However, the limited resources and technical limitations of most manufacturing companies have motivated them to outsource their RL activities to professional third-party RL providers (3PRLPs). Optimal 3PRLP selection is instrumentally valuable in RL outsourcing practices because it has the potential to increase enterprises’ economic profitability and to improve their long-term development. Generally, 3PRLP selection is treated as a multiple-attribute decision-making (MADM) problem. To this end, this paper aims to build a multi-perspective MADM (MPMADM) framework to offer systematic decision support for enterprises to select the optimal 3PRLPs. Attribute assessments in the proposed framework take the form of generalized comparative linguistic expressions (GCLEs), which can be transformed into hesitant fuzzy linguistic term set (HFLTS) possibility distributions with semantic analysis in order to enhance information quality and reliability. Expert weights are then assigned in the use of an optimization model based on the correlation consensus measurement. Afterwards, the two-stage aggregation paradigm for computing with HFLTS possibility distributions is used to gather assessments at expert and attribute levels to compile overall assessments of each alternative 3PRLP. Compared with existing studies, our proposal considers environmental and social sustainability for attribute system establishment and introduces GCLEs for 3PRLP selection, which offer greater flexibility for experts to articulate their evaluations. In addition, the two-stage aggregation paradigm eliminates distortion and loss of information and provides decision makers with the capability to control the outcome’s precision. Moreover, the proposed expert weight determination approach is conducive to generating reliable weight vectors. Several illustrative examples, sensitivity analysis, and comparative analysis further demonstrate the flexibility and practicability of our proposal.