基于犹豫模糊语言EDAS的失效模式及影响分析模型

针对传统失效模式及影响分析(FMEA)方法在复杂不确定环境下存在的缺点,提出了一种基于犹豫模糊语言集和平均解距离评价(EDAS)方法的改进FMEA模型。首先,为了准确表达专家判断的犹豫性与不确定性,采用犹豫模糊语言集表征失效模式风险评价信息;其次,考虑风险因子的个体效应和风险因子之间的交互效应,运用信息熵及交叉熵测度方法确定风险因子权重;再次,采用犹豫模糊语言EDAS方法对失效模式进行风险排序,从而识别出关键失效模式。最后,通过去哪儿网网络营销案例验证了所提模型的有效性和可行性。     

供应链失效模式风险评价排序与关联性分析

针对传统的失效模式与影响分析(FMEA)在风险评估中存在的若干缺陷,提出了基于云模型和区间二元混合加权距离测度(ITHWD)的改进FMEA供应链失效模式风险评价排序方法,这种改进FMEA方法有效解决了不同专家对决策信息的主观偏好、风险因子的相对权重问题.针对排序结果,利用复杂网络理论,构建了失效模式复杂传播网络结构关系图,对失效模式间的关联性进行了研究,系统分析了失效模式间关联关系对风险造成的不同影响.最后,通过案例供应链,验证了改进FMEA与复杂网络理论在供应链失效模式风险评价排序和失效模式间关联分析方面的可行性和有效性.     

基于FMEA的企业研发活动风险评估研究

企业研发是一项复杂的系统化活动,实施过程中往往面临着多种风险因素.在对企业研发活动进行管理的过程中,不仅要识别、分析这些因素,还要对研发风险水平进行评估,从而把有限的资源投入到关键风险因素的管理中去,提高研发活动效率.应用了FMEA对研发风险进行评估,设计出FMEA在研发风险管理中应用的具体流程.通过对企业研发活动进行分析,找出研发活动潜在失效模式及后果.然后在定义潜在失效模式严重度(S)、发生度(O)和检测度(D)等级的基础上,对各失效模式的风险优先度(RPN)进行计算,进而通过柏拉图分析确定重要的失效模式,并采取相应的防范措施,降低研发风险.     

基于改进FMEA模型的众包风险评价研究

众包作为一种新型模式,具有组织松散、参与者自由自愿、任务特性复杂等特征;这决定了企业在众包推行中存在诸多风险。失效模式与后果分析(FMEA)模型能有效识别项目风险。但传统FMEA模型存在未考虑风险因子权重、风险优先值(RPN)计算不合理等缺陷。因此,本文首先运用区间二元语义变量及区间二元混合加权距离(ITHWD)测度对FMEA模型进行改进。然后将改进的模型应用于猪八戒网"鹿寨好茶"众包项目进行实证研究。结果发现改进后的FMEA模型其可行性与有效性大大提升,能够更加客观科学的对众包风险进行排序。     

犹豫模糊偏好关系乘性一致性的判定及其在群决策中的应用

犹豫模糊偏好关系是一种有效的群决策工具。针对犹豫模糊偏好关系,提出乘性一致性、一致性指数、可接受乘性一致性等概念;获得了乘性一致性犹豫模糊偏好关系的若干判定条件。同时构造了特征犹豫模糊偏好关系,并证明了其满足乘性一致性。在此基础上,给出了不满足乘性一致犹豫模糊偏好关系的调整方法,论证了算法的收敛性。文中提出了基于犹豫模糊偏好关系的群决策模型和步骤。实例分析说明犹豫模糊偏好关系的群决策模型方法是可行和有效的。     

基于模糊集和COPRAS的改进FMEA方法

本文针对传统故障模式及影响分析(FMEA)方法在复杂不确定环境下存在的缺陷,提出了一种基于模糊集理论和COPRAS(Complex Proportional Assessment)的改进FMEA方法。该方法首先应用模糊集理论并结合专家知识建立评价故障模式的模糊语言术语集,然后由FMEA专家小组对各种故障模式根据其风险因子进行模糊评价,最后利用COPRAS方法并综合考虑风险因子的相对权重确定各故障模式的风险顺序,从而识别出关键故障模式。通过将改进FMEA方法应用到企业仓储管理中,并与传统FMEA方法结果进行比较,验证了该方法的有效性和准确性。     

FMEA方法改进及其在技术创新风险管理中的应用

在资源有限的情况下,企业技术创新风险管理的关键是主要风险的识别和防范。首先,基于合理的猜想,从优化结果角度,提出了基于可靠指标矢量-概率网格估算法的改进的FMEA方法,即在传统FMEA方法基础上,基于失效模式相关性进行失效模式替代,得出技术创新过程中的主要失效模式。其次,将改进的FMEA方法应用于一个技术创新风险管理案例,并与传统方法进行比较分析,分析结果表明:改进的FMEA方法用于风险管理不仅能节省企业资源,还能提高企业技术创新风险管理效率,是企业有效的技术创新风险管理定量分析工具。     

基于对偶犹豫模糊偏好信息的双边稳定匹配决策方法

针对基于对偶犹豫模糊偏好信息的双边稳定匹配问题,提出了一种新的匹配方法.首先,给出了基于对偶犹豫模糊偏好信息的双边稳定匹配问题的描述;然后,依据双边主体给出的偏好信息构造对偶犹豫模糊偏好矩阵,使用投影技术将对偶犹豫模糊偏好矩阵转化为满意度矩阵;接着,以双方主体满意度最大化为目标,考虑稳定匹配的约束条件,构建了匹配模型;进而,运用组合满意度分析方法,将多目标优化模型转化为单目标优化模型,通过模型求解得到最优的匹配方案;最后,实例分析说明了所提方法的实用性和有效性.     

犹豫直觉模糊偏好关系积性一致性及其在群决策中的应用

模糊偏好关系在群决策中得到了广泛研究,针对犹豫直觉模糊集既能反映决策者偏好和非偏好的信息,又能描述其犹豫心理的特点,提出了犹豫直觉模糊偏好关系及其积性一致性的定义。为了修复不一致的犹豫直觉模糊偏好关系,先构建积性一致性指标,然后提出两种修复方法。最后,将犹豫直觉模糊偏好关系应用到群决策中,通过实例和比较说明了两种修复方法的有效性和合理性。     

模糊随机环境下带个人偏好的投资组合决策模型

在现实的证券市场中,存在许多混合不确定性因素对证券的收益率产生影响.本文的目的是建立糊随机环境下带个人偏好的投资组合决策模型.在模糊随机环境下将证券的收益率视为模糊随机变量,同时考虑到投资者的偏好,提出入均值和投资者的风险曲线的概念,他们分别反映投资组合的收益和风险.文中给出新的入均值有效投资组合和入均值有效前沿的概念,探讨新的投资组合的收益率与偏好参数入的关系;最后本文采用混合智能算法进行实例分析,结果表明本文所提模型是可行性的.     

Fuzzy linear programming models for new product design using QFD with FMEA

Quality function deployment (QFD) is a customer-driven approach in processing new product developments in order to maximize customer satisfaction. Determining the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) is an important decision problem during QFD activity processes for new product development. Unlike the existing literature, which mainly focuses on the determination of DRs, this paper proposes fuzzy linear programming models to determine the fulfillment levels of PCs under the requirement to achieve the determined contribution levels of DRs for customer satisfaction. In addition, considering the design risk, this paper incorporates failure modes and effect analysis (FMEA) into QFD processes, which is treated as the constraint in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The illustration of the proposed models is performed with a numerical example to demonstrate the applicability in practice.     

The extended linear assignment method for multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets

The theory of interval-valued intuitionistic fuzzy sets is useful and beneficial for handling uncertainty and imprecision in multiple criteria decision analysis. In addition, the theory allows for convenient quantification of the equivocal nature of human subjective assessments. In this paper, by extending the traditional linear assignment method, we propose a useful method for solving multiple criteria evaluation problems in the interval-valued intuitionistic fuzzy context. A ranking procedure consisting of score functions, accuracy functions, membership uncertainty indices, and hesitation uncertainty indices is presented to determine a criterion-wise preference of the alternatives. An extended linear assignment model is then constructed using a modified weighted-rank frequency matrix to determine the priority order of various alternatives. The feasibility and applicability of the proposed method are illustrated with a multiple criteria decision-making problem involving the selection of a bridge construction method. Additionally, a comparative analysis with other methods, including the approach with weighted aggregation operators, the closeness coefficient-based method, and the auxiliary nonlinear programming models, has been conducted for solving the investment company selection problem to validate the effectiveness of the extended linear assignment method.     

区间犹豫模糊Bonferroni mean算子在多属性决策中的应用

针对在信息集成时, 需要考虑输入变量之间的相互影响以及专家评价值为区间犹豫模糊信息的多属性决策问题, 提出一种基于区间犹豫模糊Bonferroni mean算子的多属性决策方法。考虑到由于Bonferroni mean(BM)算子能够良好的反映输入变量之间相互影响, 首次提出了评价值为区间犹豫模糊集信息环境下的两种新的集成算子, 即区间犹豫模糊Bonferroni mean(IVHFBM)算子和区间犹豫模糊几何Bonferroni mean(IVHFGBM)算子。并讨论了其相关的一些特性。同时基于输入变量会具有不同重要程度的情况, 定义了区间犹豫模糊加权Bonferroni mean(IVHFWBM)算子和区间犹豫模糊加权几何Bonferroni mean(IVHFWGBM)算子。针对评价信息以区间犹豫模糊集表示的决策问题, 提出了基于IVHFWBM算子和IVHFWGBM算子的多属性决策方法。最后通过实例证明了该方法的可行性和有效性。     

基于前景理论与PROMETHEE的FMEA风险评估

针对考虑群体评价一致性和专家心理感知行为影响的失效模式及影响分析风险评估问题,提出了基于前景理论和偏好序结构排序法(PROMETHEE)的风险评估方法。首先,专家团队采用语言变量表征风险因子评价信息,利用直觉模糊熵确定风险因子客观权重;其次,采用灰关联度刻画专家评价与群体评价的一致性,以最大化关联度和极大熵准则构建专家权重优化模型;然后,集成风险因子和专家的主客观权重,获得风险因子及专家的综合权重;再次,运用直觉模糊加权平均算子集结专家评价信息,进而借助前景理论构建反应专家心理感知的前景矩阵;最后,基于PROMETHEE方法确定失效模式风险排序,并利用液晶显示器的案例验证了所提方法的有效性和可行性。     

Extensions of the multicriteria analysis with pairwise comparison under a fuzzy environment

Multicriteria decision-making (MCDM) problems often involve a complex decision process in which multiple requirements and fuzzy conditions have to be taken into consideration simultaneously. The existing approaches for solving this problem in a fuzzy environment are complex. Combining the concepts of grey relation and pairwise comparison, a new fuzzy MCDM method is proposed. First, the fuzzy analytic hierarchy process (AHP) is used to construct fuzzy weights of all criteria. Then, linguistic terms characterized by L–R triangular fuzzy numbers are used to denote the evaluation values of all alternatives versus subjective and objective criteria. Finally, the aggregation fuzzy assessments of different alternatives are ranked to determine the best selection. Furthermore, this paper uses a numerical example of location selection to demonstrate the applicability of the proposed method. The study results show that this method is an effective means for tackling MCDM problems in a fuzzy environment.     

A way to deal with fuzzy preferences in multi-criteria decision problems

This paper presents a special multiple criteria decision making approach for solving problems in context with fuzzy individual preferences.At first we briefly expose the proposed methodology. The individual preferences are explicitly given by a complete transitive relation R on a set of reference actions. The modelling of the decision-maker's preferences is obtained by means of fuzzy outranking relations. These fuzzy relations are based on a system of additive utility functions which are estimated by means of ordinal regression methods analysing the preference relation R.This is followed by a presentation of two real multicriteria problems which the proposed methodology has been applied to, i.e. a highway plan choice problem and a problem in marketing research dealing with the launching of a new product. In each application we tried to specify this method according to the specific structure of the problem considered.     

Note on “Some models for deriving the priority weights from interval fuzzy preference relations”

Deriving accurate interval weights from interval fuzzy preference relations is key to successfully solving decision making problems. Xu and Chen (2008) proposed a number of linear programming models to derive interval weights, but the definitions for the additive consistent interval fuzzy preference relation and the linear programming model still need to be improved. In this paper, a numerical example is given to show how these definitions and models can be improved to increase accuracy. A new additive consistency definition for interval fuzzy preference relations is proposed and novel linear programming models are established to demonstrate the generation of interval weights from an interval fuzzy preference relation.     

基于模糊证据推理和灰色关联理论的FMEA方法

在对传统FMEA研究的基础上,提出了基于模糊证据推理和灰色关联理论的FMEA方法.结合模糊证据推理理论建立评价故障模式的模糊王信结构,对各种故障模式进行评价,利用灰色关联理论计算各种故障模式的关联度,通过排序来确定故障模式的风险顺序.该方法克服了传统FMEA的缺陷,提高了FMEA应用的准确性和可靠性.