Weights Of Experts Research Articles

A new approach to fuzzy group decision making with trapezoidal fuzzy preference relations by using compatibility measure

This paper aims to develop a new approach to deal with fuzzy group decision making (GDM) with additive trapezoidal fuzzy preference relations (ATFPRs) by using compatibility measure. We firstly present some concepts of compatibility index and expected preference relation (PR) for ATFPR and then propose a compatibility improving algorithm to help each individual PR achieve acceptable compatibility . Moreover, a least deviation model is provided to obtain the priority vector. Besides, based on the criterion of minimizing the compatibility index, we put forward an optimal model to determine the weights of experts in GDM. Finally, the GDM process with compatibility of ATFPRs is presented, and an illustrative example is utilized to verify the developed approach . The main features of our approach are that: (1) It guarantees that each individual ATFPR is acceptably compatible by using compatibility improving algorithm. (2) It ensures that experts’ weights in group aggregation are determined objectively by optimal model.

Generalized Choquet Integral Operator of Triangular Atanassov’s Intuitionistic Fuzzy Numbers and Application to Multi-Attribute Group Decision Making

The purpose of this paper is to propose a new approach to interactive multi-attribute group decision making with triangular Atanassov's intuitionistic fuzzy numbers (TAIFNs). The contribution of this study is fivefold: (1) Minkowski distance between TAIFNs is firstly defined; (2) We define the possibility attitudinal expected values of TAIFNs and thereby present a novel risk attitudinal ranking method of TAIFNs which can sufficiently consider the risk attitude of decision maker; (3) The weighted average operator (TAIFWA) and generalized ordered weighted average (TAIFGWA) operator of TAIFNs are defined as well as the hybrid ordered weighted average (TAIFHOWA) operator; (4) To study the interaction between attributes, we further develop the generalized Choquet (TAIF-GC) integral operator and generalized hybrid Choquet (TAIF-GHC) integral operator of TAIFNs. Their desirable properties are also discussed; (5) The individual overall value of alternative is obtained by TAIF-GC operator and the collective one is derived through TAIFWA operator. Fuzzy measures of attribute subsets and expert weights are objectively derived through constructing multi-objective optimization model which is transformed into the goal programming model to solve. The system analyst selection example verifies effectiveness of the proposed approach.

Model for Software Patterns Selection with Fuzzy Linguistic Information

With respect to multiple attribute group decision making problem with triangular fuzzy linguistic information, in which the attribute weights and expert weights take the form of real numbers, and the preference values take the form of triangular fuzzy linguistic variables, some operators for aggregating triangular fuzzy linguistic variables, such as the fuzzy linguistic harmonic mean (FLHM) operator, fuzzy linguistic weighted harmonic mean (FLWHM) operator, fuzzy linguistic ordered weighted harmonic mean (FLOWHM) operator, and fuzzy linguistic hybrid harmonic mean (FLHHM) operator are proposed. Based on the FLWHM and FLHHM operators, a practical method is developed for group decision making with triangular fuzzy linguistic variables. Finally, an illustrative example about software patters selection is given to verify the developed approach.

Fuzzy comprehensive evaluation based on multi-attribute group decision making for business intelligence system

Business Intelligence System (BIS) has become an important tool for enterprises to make decision timely and effectively. However there are many differences in the quality and performance of the BIS on the market, it is necessary for enterprise managers to evaluate the BIS before buying, so that they could choose the right BIS. This study provided a fuzzy comprehensive evaluation method based on multi-attribute group decision making for selecting BIS. Eight evaluation criteria about BIS were firstly determined through literature review with three judgment methods being presented to score these criteria. Then the ordered pairwise comparison method was used to determine the weight of criteria, and the entropy measure method of interval-valued intuitionistic fuzzy sets was applied to determine the weights of experts. A fuzzy comprehensive evaluation algorithm based on multi-attribute group decision making was proposed, which we used to select suitable supplier of BIS. Finally an illustrative supplier selection problem was described to demonstrate the practicality and effectiveness of the proposed method.

Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.

АЛГОРИТМЫ И ПОРЯДОК ПРОВЕДЕНИЯ ЭКСПЕРТИЗЫ ПО ОЦЕНКЕ ВЛИЯНИЯ ВНЕШНЕЙ СРЕДЫ НА РЕГИОНАЛЬНУЮ СОЦИАЛЬНО-ЭКОНОМИЧЕСКУЮ СИСТЕМУ ДЛЯ ФОРМИРОВАНИЯ БАЗЫ ЗНАНИЙ СОВЕТУЮЩЕЙ ИНФОРМАЦИОННОЙ СИСТЕМЫ

The examination for the estimate of the environment effect upon a regional social-economic system for the formation of a knowledge base of a corresponding information system is a sequence of separate procedures. Expert assessments are processed in the corresponding information system (CIS). In the paper there is shown an algorithm for a computation of an expert weight at the formation of a knowledge base of a corresponding information system, and also a mathematical apparatus of expert response processing is presented. The author’s experience in the application of the expert assessment method in the estimate of the presence both a connection force between environment, and a regional social-economic system. For the assessment of the effect force as a mathematical apparatus the theory of a fuzzy set was used. The expert response processing has shown that 487 variants of connections under analysis can be divided into groups of a strong, medium and weal effect taking into account their belonging to the groups Sm . The computation of the concordance factor W and checkup for the expert response co-ordination of this stage of the examination have shown a high degree of coordination of expert opinions. The amount of 445 connections obtained as a result of the examination characterizing a strong and medium influence of environment upon a regional social-economic system were loaded into a knowledge base of CIS as the most sig-nificant ones. The CIS processes and analyzes exami-nation results after which it offers the selection of concrete measures of a management type allowing either increasing, or grading their influence depending on the context. Therefore, the developed CIS increases the efficiency of processing and analysis of expert assessments, which on the whole ensures effective decisionmaking by managers and specialists.

Consensus models based on distance for interval fuzzy and multiplicative preference relations

This paper presents consensus models based on distance for group decision making problems under interval fuzzy and multiplicative preference relations. First, some quadratic programming models based upon the idea of minimizing the sum of squared distances between all pairs of weighted interval fuzzy or multiplicative preference judgments are developed to obtain the weights of experts. Then, two indices, an individual to group consensus index (ICI) and a group consensus index (GCI) are defined. Furthermore, iterative consensus algorithms are proposed and the processes stop until both the ICI and GCI are less than predefined thresholds or reaching the maximum number of iteration. Finally, two illustrative examples are given to demonstrate the feasibility and effectiveness of the proposed methods.

Online Portfolio Selection Strategy Based on Combining Experts’ Advice

The weak aggregating algorithm (WAA) developed from learning and prediction with expert advice makes decisions by considering all the experts' advice, and each expert's weight is updated according to his performance in previous periods. In this paper, we apply the WAA to the online portfolio selection problem. We first consider a simple case in which the expert advice is the strategy for investing in one stock; for this case, we obtain a portfolio selection strategy WAAS and prove that the WAAS can identify the best stock. We also discuss a more complicated case in which constant rebalanced portfolios are considered as expert advice, and obtain a corresponding portfolio selection strategy WAAC. The theoretical result shows that the cumulative gain that WAAC achieves is as large as that of the best constant rebalanced portfolio. Numerical analysis shows that the cumulative gains of our proposed strategies are as large as those of the best expert advice.

An improved FMEA method based on the linguistic weighted geometric operator and fuzzy priority

ABSTRACTUsing the group linguistic information aggregation method, this research improved upon conventional failure mode and effect analysis (FMEA), which had the problems of the inevitable subjectivity of expert evaluations, and of the difficulty of determining the weights of experts' evaluations. First, this research treated the three risk factors of FMEA evaluation as linguistic variables and introduced a linguistic weighted geometric (LWG) operator to implement algebraic operations on the results of expert evaluations. This treatment overcame the disadvantage of fuzzy theory-based methods in which decision-making information could be lost due to the twice conversion process. Second, the weights of experts' evaluations were calculated based on the consistency of experts' ranking using a fuzzy priority method. This method placed a lower weight for the experts whose evaluation departed from group consensus, and therefore decreased the impact of the unfairness of experts on evaluation results. Finally, this article demonstrated the effectiveness and applicability of this method by an example of failure-mode analysis on the grinding wheel system of a numerical control machine.

Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing

In this paper, the effect of weighting strategies on sustainability performance assessment is addressed. Eco-efficiency is used as the main metric for sustainability performance evaluation. An integrated input-output life cycle assessment (LCA) and multi criteria decision making (MCDM) approach is employed. The US manufacturing sectors’ LCA results are used in conjunction with the proposed MCDM framework to perform the eco-efficiency evaluation of 276 US manufacturing sectors. Five environmental impact categories are considered as the negative factors, namely: greenhouse gas emissions, energy use, water withdrawal, hazardous waste generation and toxic releases into air and the economic output of each manufacturing sector is considered to be the positive output. To study the overall impact of different weighting strategies; twenty weighting scenarios are designed. Five pairs of weights considered for the overall economic versus environmental impacts along with four specific weighting strategies based on Harvard, SAB, EPP and Equal weighting for each pair. According to the results of the statistical analysis, it is concluded that the weighing strategies applied to the overall environmental impacts and economic outputs cause statistically significant differences in the eco-efficiency scores.

Application of fuzzy integral theory in group decision-making analysis

Based on the principle of group decision-making, a group decision-making model was built up by fuzzy integral in this paper after comprehensively considering the expert weight difference. At the same time, the scientific and rationality of this model were analyzed in theory. At last, the practicability of the method was proved by the application example, it provides a practical decision method for the group decision problems.

A new method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers

Purpose – The triangular intuitionistic fuzzy number (TIFN) is very useful for expressing ill-known quantity. The purpose of this paper is to develop a new method for multi-attribute group decision-making (MAGDM) problems, in which the attribute values are the TIFNs, the attribute weights are completely unknown and the weights of decision makers are given by linguistic variables. Design/methodology/approach – A new method is given to rank TIFNs based on the weighted possibility mean and standard deviation of TIFNs. The weighted Minkowski distance of TIFNs is defined by using the weighted lower and upper possibility means of TIFNs. The weights of experts are determined in terms of the voting model of intuitionistic fuzzy set (IFS). The weights of attributes can be objectively determined through utilizing the information entropy defined by weighted Minkowski distance of TIFNs. Through integrating the attribute weights and expert weights, the collective comprehensive ranking values of alternatives are obtained and used to rank the alternatives. Findings – The stock selection example and comparison analysis show the validity and applicability of the method proposed in this paper. Originality/value – The paper presents a new ranking method of TIFNs and defines the weighted Minkowski distance of TIFNs. The weights of experts are determined in terms of the voting model of IFS. The weights of attributes can be objectively determined through utilizing the information entropy. The proposed method can greatly enhance the flexibility and agility of decision-making process.

Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information

A novel social network based group decision making (SN-GDM) model with experts’ weights not provided beforehand and with the following four tuple information: trust; distrust; hesitancy; and inconsistency, is introduced. The concepts of trust score (TS) and knowledge degree (KD) are defined and combined into a trust order space. Then, a strict trust ranking order relation of trust function values (TFs) is built in which TS and KD play a similar role to the mean and the variance in statistics. After the operational laws of TFs for uninorm operators are built, the uninorm propagation operator is investigated. It can propagate through a network both trust and distrust information simultaneously and therefore it prevents the loss of trust information in the propagating process. When an indirect trust relationship is built, the uninorm trust weighted average (UTWA) operator and the uninorm trust ordered weighted average (UTOWA) operator are defined and used to aggregate individual trust relationship and to obtain their associated ranking order relation. Hence, the most trusted expert is distinguished from the group, and the weights of experts are determined in a reasonable way: the higher an expert is trusted the more importance value is assigned to the expert. Therefore, the novelty of the proposed SN-GDM is that it can use indirect trust relationship via trusted third partners (TTPs) as a reliable resource to determine experts’ weights. Finally, the individual trust decision making matrices are aggregated into a collective one and the alternative with the highest trust order relation is selected as the best one.

Multi-criteria decision analysis of offshore wind turbines support structures under stochastic inputs

Selection of the optimum support structural configuration for offshore wind turbines is a decision that should take into account a variety of both technical and non-technical criteria in order to aggregate cumulatively the performance of several concepts such as jackets, tripods, monopiles, spars, semi-subs, tri-floaters and others with varying suitability and relevance to offshore wind applications. In the preliminary phases of a project, consideration of qualitative attributes based on experts’ opinions is a common practice due to the limited availability of quantitative data for attribute scores and relevant weights, which are mainly based on past experience. This practice can introduce a bias in the considered scores reducing the confidence of the qualifying solution. This paper documents the extension of the widely used TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) to explicitly consider stochastic inputs (statistical distributions) following a systematic gathering of data through surveys and questionnaires. The methodology proposed has been efficiently implemented in a numerical tool and the case study that is presented on the decision analysis of an offshore wind turbine supports structure under given deployment conditions, illustrates its applicability and allows for a sensitivity analysis of the effect of the resolution of simulation, selection of statistical distributions and weighting of experts’ opinions based on their perceived level of expertise.

An Interval- Valued Hesitant Fuzzy Ranking Method based on Group Decision Analysis for Green Supplier Selection

Selecting the most suitable green supplier is one of the main issues for companies. In this paper, an interval-valued hesitant fuzzy ranking method is proposed to rank the green suppliers candidates under conflicted criteria. Hence, the weight of each expert is obtained by extended entropy method based on interval-valued hesitant fuzzy set (IVHFS) approach. In addition, the criteria weights are determined based on preference judgments of experts and the effect of experts' weights. Considering the IVHFS allows the experts to determine some interval-values membership degrees for each green supplier under a set and also applying the criteria and experts weights could lead to a precise approach. Finally, an illustrative example of the green supplier selection is provided to show the validity and applicability of the proposed method.

Aggregating evaluation using dynamic weighted intuitionistic fuzzy approach for concept sequencing in an e-learning system

Adaptation of learning concepts in an e-learning system for individuals depends on his ability to learn. Learners are perplexed in hyperspace when learning concepts are displayed in an unstructured form. To provide an effective learning path, sequencing of learning concepts and contents should be according to learner's orientation and individualised access support. Therefore to reduce disorientation, each learner should get path based on his level of knowledge acquisition and learning style. However, most of the e-learning systems determine the learner's knowledge through expert weight given to a learning concept whereas in this work weights of learning concepts are determined during learners' assessment itself. For this, we applied intuitionistic fuzzy theory approach and proposed a dynamic weighted concept intuitionistic fuzzy averaging (DWCIFA) operator to personalise the sequencing of learning concepts based on assessment results. We tested the algorithm on synthetic data set, created using quasi random generator function in Python. The results prove that it improves navigation, and also helps student to achieve deep knowledge in the domain.

Reliability-oriented Complexity Analysis of Manufacturing Systems Based on Fuzzy Axiomatic Domain Mapping

Decreasing the complexity is the effective way to improve the reliability of manufacturing systems. However, the research on the reliability oriented complexity analysis for manufacturing system is rare. A reliable manufacturing system is a prerequisite to ensure the well-designed products be manufactured faultlessly. In this context, this paper proposes an axiomatic complexity analysis model of manufacturing system based on the fuzzy axiomatic domain mapping. Firstly, in terms of the uncertainty of domain mapping information, the fuzzy evaluation matrix for complexity which integrated the weights of experts is constructed based on triangular fuzzy numbers. Secondly, considering the influence of the design parameters (DPs) on the functional requirements (FRs), a quantitative computation approach to manufacturing system complexity is achieved by means of fuzzy evaluation matrix. Finally, a case study is presented to illustrate the validity of the proposed method.

A group decision making approach for trapezoidal fuzzy preference relations with compatibility measure

The purpose of this paper is to develop a new compatibility for the additive trapezoidal fuzzy preference relations and utilize it to determine the optimal weights of experts in the group decision making. First, a least deviation model to obtain the priority vector of the additive trapezoidal fuzzy preference relation is provided. Then compatibility index of two additive trapezoidal fuzzy preference relations is proposed and some desirable properties are investigated. The characteristic of the new compatibility is that it uses the deviation measure between an additive trapezoidal fuzzy preference relation and its characteristic preference relation based on consistency of the preference relation, which develops a theoretic basis for the application of additive trapezoidal fuzzy preference relations in group decision making. Then, in order to determine the weights of experts in the group decision making, we propose an optimal model based on the criterion of minimizing the compatibility index. Finally, an example shows the feasibility and effectiveness of the proposed method.

A Multigranularity Linguistic Group Decision-Making Method Based on Hesitant 2-Tuple Sets

Hesitant fuzzy linguistic term set HFLTS is a very useful technology in dealing with decision-making problems where people have hesitancy in providing their linguistic assessments. Distinct methods have been developed to aid decision making with HFLTSs, yet there is little research involving the issue that how to deal with the multigranularity hesitant fuzzy linguistic information. The aim of this paper is to develop the aggregation method for multigranularity hesitant fuzzy linguistic information and solve the linguistic group decision problem with different linguistic term sets. To do so, we first modify the translation functions and aggregation operators in the existing 2-tuple linguistic representation models so as to aggregate linguistic terms from different linguistic term sets. Then, we introduce the notion of hesitant 2-tuple sets to make computation of HFLTSs without loss of information, and develop some new operators to aggregate HFLTSs from different linguistic term sets. Using these operators, we propose a method to deal with multigranularity linguistic group decision-making problems with different situations where importance weights of either criteria or experts are known or unknown. Finally, the multigranularity linguistic group decision-making model is implemented to the healthcare waste treatment in West China Hospital to validate its effectiveness and efficiency in aiding decision-making process.

New Min-Max Approach to Optimal Choice of the Weights in Multi-Criteria Group Decision-Making Problems

In multi-criteria group decision-making (MCGDM), one of the most important problems is to determine the weights of criteria and experts. This paper intends to present two Min-Max models to optimize the point estimates of the weights. Since each expert generally possesses a uniform viewpoint on the importance (weighted value) of each criterion when he/she needs to rank the alternatives, the objective function in the first model is to minimize the maximum variation between the actual score vector and the ideal one for all the alternatives such that the optimal weights of criteria are consistent in ranking all the alternatives for the same expert. The second model is designed to optimize the weights of experts such that the obtained overall evaluation for each alternative can collect the perspectives of the experts as many as possible. Thus, the objective function in the second model is to minimize the maximum variation between the actual vector of evaluations and the ideal one for all the experts, such that the optimal weights can reduce the difference among the experts in evaluating the same alternative. For the constructed Min-Max models, another focus in this paper is on the development of an efficient algorithm for the optimal weights. Some applications are employed to show the significance of the models and algorithm. From the numerical results, it is clear that the developed Min-Max models more effectively solve the MCGDM problems including the ones with incomplete score matrices, compared with the methods available in the literature. Specifically, by the proposed method, (1) the evaluation uniformity of each expert on the same criteria is guaranteed; (2) The overall evaluation for each alternative can collect the judgements of the experts as many as possible; (3) The highest discrimination degree of the alternatives is obtained.