A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis techniques due to its outstanding capabilities in identifying, assessing, and eliminating potential failure modes in a wide range of industrial applications. It provides a comprehensive view for investigating potential failures, causes, and effects in designs, products, and processes. However, traditional FMEA is extensively criticized for its defects in determining the criteria weights, identifying the risk priority of failure modes, and handling the uncertainty during the risk evaluation. To resolve these problems, this study proposes a novel fuzzy rough number extended multi-criteria group decision-making (FR-MCGDM) strategy to determine a more rational rank of failure modes by integrating the fuzzy rough number, AHP (analytic hierarchy process), and VIKOR (Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje). Above all, a fuzzy rough number is introduced to characterize experts’ judgment, aggregate group risk assessments, and tackle the uncertainty and subjectivity in the risk evaluation. Then a fuzzy rough number enhanced AHP is presented to determine the criteria weights. A fuzzy rough number enhanced VIKOR is proposed to rank the failure modes. A practical case study of the check valve is provided to validate the applicability of the proposed FMEA. Comparative studies demonstrate the efficacy of the proposed FR-MCGDM, with remarkable advantages in handling the uncertainty and subjectivity during failure modes evaluation.     

Design concept evaluation considering information reliability,uncertainty, and subjectivity: An integrated rough-Z-number-enhanced MCGDM methodology

Design concept evaluation plays a vital role in new product development due to its critical impact on successive design activities as well as the novelty and competence of the final product. Lots of decision-making techniques have been developed for the design concept evaluation under various contexts. However, existing methods mainly focus on the evaluation under precise environments or fuzzy set scenarios. Whereas in reality, most risk assessments are derived from experts’ subjective judgments. The reliability of the evaluation is rarely taken into consideration. To bridge this gap, this study proposes a rough-Z-number-enhanced MCGDM (multi-criteria group decision-making) to resolve the design concept evaluation considering information reliability, uncertainty, and subjectivity. A rough-Z-number is introduced to incorporate into the MCGDM framework to represent the individual evaluation and aggregate group risk assessments. A rough-Z-number-based AHP (analytic hierarchy process) is presented to determine the criteria weights. A rough-Z-number-based MABAC (multi-attributive border approximation area comparison) is proposed to rank the design concepts. Experiments and comparative analyses demonstrate the advantage of the rough-Z-number extended MCGDM in complex and uncertain decision-making environments. With the rough-Z-number-based enhancement, the information reliability, uncertainty, and subjectivity in design concept evaluation are well characterized.     

A customer-involved design concept evaluation based on multi-criteria decision-making fusing with preference and design values

Customer-involved design concept evaluation (CDCE) is a key issue for developing new product welcomed by customers, but seldom studies have considered the integrated utilization of objective design values (DVs) and customers’ subjective preference values (PVs). Our previous study has attempted to fuse with DVs and PVs in CDCE, while this is only limit to few situations under benefit-like and cost-like evaluation criteria. For better CDCE, this study further fuses with DVs and PVs in more complex situations, and puts forward an improved version of rough distance to redefined ideal solution (RD-RIS II) in multi-criteria decision-making (MCDM) scope to select the optimal concept. Different from old RD-RIS in previous study, RD-RIS II not only supports the ideal solution definition (ISD) processes for both quantitative and qualitative evaluation criteria, and but more importantly, it utilizes more useful information (value, feature, number and impact) from DVs and PVs to redefine the new positive ideal solution (PIS) and negative ideal solution (NIS). Through the rough distance calculation, the alternative which is close to PIS and far away from NIS is selected as the best one. Besides, the feasibility of RD-RIS II is validated via the application in real design evaluation example, and three empirical comparisons confirm that RD-RIS II makes more comprehensive decision than other MCDM-based evaluation methods, especially when the choices of customers and designers are conflicting, therefore it could provide more reasonable evaluation result which has better credibility and stability than others.     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

A novel hybrid MCDM model based on fuzzy DEMATEL,fuzzy ANP and fuzzy VIKOR for city logistics concept selection

City logistics (CL) tends to increase efficiency and mitigate the negative effects of logistics processes and activities and at the same time to support the sustainable development of urban areas. Accordingly, various measures and initiatives are applying and various conceptual solutions are defining. The effects vary depending on the characteristics of the city. This paper proposes a framework for the selection of the CL concept which would be most appropriate for different participants, stakeholders, and which would comply with attributes of the surroundings. CL participants have different, usually conflicting goals and interests, so it is necessary to define a large number of criteria for concepts evaluation. On the other hand, the importance of the criteria is dependent on the specific situation, i.e., a large number of factors describing the surroundings. In situations like this, selecting the best alternative is a complex multi-criteria decision-making (MCDM) problem consisting of conflicting and uncertain elements. A novel hybrid MCDM model that combines fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), fuzzy Analytical Network Process (ANP) and fuzzy Višekriterijumska Optimizacija i kompromisno Rešenje (VIKOR) methods is developed in this paper. The model provides support to decision makers (planners, city administration, logistics service providers, users, etc.) when selecting the CL concept, which is successfully performed in this paper for the City of Belgrade.     

An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data

Selecting the optimal design scheme is a vital task in the product design area. It not only improves the performance of the product, but also leads to the greatest satisfaction of customers. However, existing methods express qualitative evaluation information roughly, and none of them has taken the implicit psychological states of customers into consideration. Therefore, an integrated decision-making method for product design scheme evaluation is proposed. This method applies the cloud model to facilitate the evaluation process of experts and uses the EEG data to reveal the psychological states of customers. Benefit from the probability theory and fuzzy set theory, the cloud model deals with the fuzziness and randomness simultaneously. It can decrease the cognitive discrepancy of experts and allow the information distortion to be neutralized to a great extent. Since the experts are not the final users of products, the evaluation results from experts cannot truly reflect the psychological states of customers when they use the product. An experiment is designed to collect the EEG data which can reveal the implicit psychological states of customers. The recorded data are segmented based on the operation process and tagged with the self-reported psychological states. Subsequently, the wavelet packet decomposition is applied and the sample entropy of each EEG frequency band is extracted as the feature. Taking advantage of the random forest classifier, the psychological states of customers can be classified with the average accuracy of 90.76%. This study can lead to a practical system for automatic assessment of psychological states in future applications. The evaluation process of elevator design schemes is conducted as a case study to illustrate the feasibility of the proposed method.     

基于层次分析法的SCADA系统安全评价

随SCADA系统在电力、石油等工业领域和其他领域的广泛应用,以及计算机网络与通信技术的发展,人们越来越关心SCADA系统的安全问题。文章从SCADA系统的安全性入手,分析了SCADA系统的脆弱性,同时介绍了安全评价的主要方法,最后通过层次分析法对油气SCADA系统进行了综合评价。     

基于层次分析法和二元语义的供应商评价研究

供应商评价是一个多指标群决策问题。针对语言评价信息的模糊性,提出了一种基于层次分析法和二元语义的评价方法。利用层次分析法确定供应商评价指标权重,采用近年来最新发展的二元语义和算子进行信息的集结,给出了供应商评价选择的计算步骤。最后,算例分析证明了该方法的简便性和有效性。     

基于区间灰数的MAS任务分配模型及VIKOR方法

在引入区间灰数范数的基础上,建立了基于区间灰数的多属性多Agent系统（MAS）任务分配模型,提出了基于区间灰数的扩展VIKOR方法及多属性MAS最优任务分配原理,并对该任务分配算法进行有效的实证分析。实证分析说明,这种方法是非常有效且能够做出比较合理的任务分配决策。     

基于AHP的高校网站模糊综合评价

本文建立了我国高校网站评价的指标体系,并用指数标度的层次分析法确定评价指标的权重,并结合实际用模糊综合评价法研究了高校网站的评价问题.     

基于AHP模型的政府系统职能评价方法的研究

政府人力资源系统与政府职能密切相关,研究政府人力资源必须从政府职能的视角对政府系统的要素与职能的关系进行分析.在政府系统的环境适应性概念模型的基础之上,通过对政府系统的政治职能、经济职能、社会职能、文化职能的分析,研究了政府系统要素与职能、规模关系,明确了政府系统的目标是政府系统内部结构分析的出发点,是政府系统目的的具体化的特征,确定了政府系统的目标集,构建了政府职能评价的递级层次结构模型,并通过AHP方法,构建了政府系统职能评价模型.     

A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.     

Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach

Evaluating teaching performance is a main means to improve teaching quality and can plays an important role in strengthening the management of higher education institutions. In this paper, we present a novel framework for teaching performance evaluation based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method. Specifically, after determining the factors and sub-factors, the teaching performance index system was established. In the index system, the factor and sub-factor weights were then estimated by the extent analysis fuzzy AHP method. Employing the fuzzy AHP method in group decision-making can facilitate a consensus of decision-makers and reduce uncertainty. On the basis of the system, the fuzzy comprehensive evaluation method was employed to evaluate teaching performance. A case application was also used to illustrate the proposed framework. The application of this framework can make the evaluation results more scientific, accurate, and objective. It is expected that this work may serve as an assistance tool for managers of higher education institutions in improving the educational quality level.     

New customer-oriented design concept evaluation by using improved Z-number-based multi-criteria decision-making method

The customer-oriented design concept evaluation (CDCE) enables companies to select the best design concept from the perspective of customer to win the customer-centered market. However, previous CDCE studies only focus on the customer’s preference value (PV), but neglect the customer’s confidence attitude on this preference, i.e., the preference reliability (PR), and some design specifications, e.g., the design attribute’s importance (DAI). To address such drawbacks, we propose a new CDCE by using improved Z-number-based multi-criteria decision-making (IZ-MCDM) method to better express and utilize customer’s uncertain opinion. In IZ-MCDM, the Z-number is used to express the customer’s opinion (Z-opinion) that includes PV and its affiliated PR information. Z-opinion is translated into an interval Z-number to form a new type of evaluation value and decision matrix. Based on the evaluation value, a new ideal solution selection (ISS) strategy integrating with PV, PR and DAI information is employed in IZ-MCDM. By comparing with the re-defined ideal solution, the alternative that attracts certain high-preferences for its importance attribute values and uncertain low-preferences for its less importance attribute values is more likely to be recommended as the best one. Hence, IZ-MCDM can get more reasonable design concept than classical PV-only CDCE method. Two empirical experiments from existing CDCE examples have been carried out in this study, and the comparison experimental results further validate the significance of IZ-MCDM, which show that 1) besides PV factor, PR and DAI factors could also significantly impact the evaluation result; 2) these two factors should be acted together to select the ideal solution; 3) IZ-MCDM has suitability as it supports different MCDM models with different deviation measurement metrics to evaluate the alternatives.     

主客观权重集成及扩展VIKOR的多属性群决策方法

针对评价信息、属性权重均为不同粒度语言短语的多属性群决策问题,提出一种基于主客观权重集成及扩展多准则协调优化解（VIKOR）的多属性群决策方法。由基本语言评价集实现对多粒度语言评价矩阵的一致化,基于同一粒度的语言决策矩阵计算群体对属性的评价偏差,基于群体评价意见的一致性原则得到属性客观权重,通过二元语义加权算术平均（T-WAA）算子得到属性主观权重,从而集成主、客观权重求得属性综合权重。集结转化后的单个评价矩阵得到群体评价矩阵及其导出矩阵,由扩展VIKOR方法,根据群效用值、个体遗憾值及综合评价值分别对方案进行排序,获得折衷方案。算例分析表明该方法的有效性与可行性。     

一种基于三端点区间数的改进DS/AHP方法

为了解决传统DS/AHP方法不适用于具有多位专家或专家意见富有分歧的群体决策问题,基于能够有效描述专家群组推理判断信息的三端点区间数对传统判断矩阵进行拓展,提出三端点区间数知识矩阵,基于三端点区间数中的一致性原理构建了用于从该矩阵中提取证据信息的基本概率分配函数的确定模型,在此基础上依据Dempster组合规则提出改进DS/AHP方法的操作步骤,应用一个数值模拟实例演示了该方法的具体操作过程。     

基于Fuzzy AHP的司钻控制房人机界面评价

为了对石油钻机司钻控制房人机界面进行科学的评价,在确定其评价指标的基础上,运用Fuzzy AHP（模糊-层次分析）综合评价法实现了定性指标的定量转换,建立了石油钻机司钻控制房人机界面评价模型,克服了评价的主观性。根据上述理论编制了评价系统,避免了评价过程中的大量人工数学计算。研究表明,利用该方法建立的评价系统可以方便有效地实现对石油钻机司钻控制房人机界面的评价。     

基于AHP和模糊综合评判的信息安全风险评估

从信息安全风险评估的原理和研究现状入手,提出了基于层次分析法（AHP）和模糊综合评判的信息安全风险评估的方法,解决了风险评估中定性指标定量评估的难点。最后给出实例,证明该方法能有效地应用于信息安全风险评估。     

云计算环境下基于灰色AHP的供应商信任评估研究

针对传统信任模型的不足,以改善供应链企业间的信任危机为目标,以灰色系统理论为基础,将层次分析法(analytic hierarchy process,AHP)与灰色评估法相结合,提出一种基于灰色AHP的云计算供应商信任评估模型。通过定义信任评价等级灰类及白化权函数,计算云环境下供应商企业实体各信任证据的灰色评价权,结合层次分析法构建层次结构并确定信任证据权值,最后求出供应商企业的信任评估值及信任等级灰类。实例说明,该信任评估方法能够有效应用于云环境供应商评价中,评价结果客观有效。