基于D-S证据理论的测试性分配方法研究

针对现有测试性分配方法的不足,以及人们判断的模糊性和不确定性这两个问题,将模糊层次分析法引入测试性分配中,先求得层次单排序权重向量,进而求得层次总排序权重向量,根据总排序权重向量求得各组成单元的分配指标。针对传统模糊层次分析法在融合多个专家的评价时只是进行简单的数据相加再求平均这一不足之处,将D-S证据理论运用到融合不同专家的评价中,最后结合案例应用验证该方法的可行性。     

基于三角模糊数的测试性分配方法

在考虑多因素影响的测试性分配中,需要专家通过两两比较来确定诸因素的相对重要性．然而由于人们的判断具有模糊性和不确定性,故在构造比较判断矩阵时所给出的判断往往不是确定的数值．针对这一问题,本文提出基于三角模糊数的测试性分配方法．该方法用三角模糊数表示专家评判数值范围,考虑人判断的模糊性,将无弹性的硬指标转化成模糊的软指标,建立基于三角模糊数的结构模型,同时结合层次分析（APH）方法进行指标权重的确定,由得到的权重进行测试性指标的分配．最后结合实例对本方法进行应用研究,应用结果验证了此方法的可行性．     

基于改进AHP和模糊评价的装备测试性参数选择

针对装备实际工程应用测试性参数多依靠专家经验或行业常规进行选择,导致所选参数适用范围小、随机性大等问题,提出改进的层次分析法(analytical hierarchy process,AHP)和模糊评价相结合的测试性参数选择方法。即依据装备可靠性、维修性和可用性初选出测试性参数,并将使用要求作为测试性参数定性评价指标,采用改进AHP方法计算评价指标因子权重,在此基础上采用模糊评价法计算测试性参数优先度,优选出测试性参数集,从而为测试性指标的制定奠定基础。最后以综合传动装置为例,采用该方法优选出精简的测试性参数集。     

基于遗传算法的测试性优化分配方法

测试性分配是测试性设计中的一项重要环节.测试性分配参数主要是故障检测率(FDR)和故障隔离率(FIR).常用的权值分配法存在权值计算繁杂、分配结果需要调整等不足.优化分配是一种较好分配方法,它是在一系列的约束条件下,使分配的结果费效比最优.但是解决约束优化分配问题的内点法、最小乘子法等算法存在易陷入局部最优、寻找初始点...     

基于贡献度的测试性验证试验样本分配方案研究

针对现有基于故障率的测试性验证试验分层抽样方案中,样本集代表性较差的问题,提出基于可更换单元贡献度的样本分配方案。首先在全面分析样本分配主要影响因素的基础上,定义可更换单元的特性向量和贡献度;其次利用递阶质量屋求解贡献度,并给出样本分配方案;最后通过实例验证方案的可行性。     

测试性验证故障注入方法

由于装备测试性的重要性,急需一种有效的测试性验证方法。分析技术研究现状并结合实际提出基于通用测试性验证系统的实用测试性验证故障注入方法,对系统组成原理、关键技术进行了描述。     

基于层次分析法的计量测试系统的效能评价

随着计量技术的发展,智能化现代计量测试系统正逐渐取代传统的计量测试仪器,因此对计量测试系统进行优劣分析和效能评价是十分必要的。层次分析法就是解决这类复杂问题的定性和定量相结合的评价分析方法,具有科学性、完整性和系统性,其评价的步骤是建立层次结构模型、构造比较矩阵、计算单排序向量并进行一致性检验、计算总排序权重及评价。该文利用层次分析法对计量测试系统进行分析和评价,通过实例对现代计量测试系统和传统计量测试仪器的效能进行评价验证。     

基于故障属性的测试性验证试验样本分配方案

故障样本分配方案是测试性验证试验的关键技术之一.针对现有基于故障率的按比例分层抽样分配方案试验结果不可信的问题,提出基于故障属性的测试性验证试验样本分配方案.该方案在综合考虑故障率、故障后果和故障被检测难度等故障属性元素的基础上,建立了故障属性模型和故障属性值计算模型,并根据故障属性值相对比值的方法进行样本分配.案例应用结果表明:与基于故障率的按比例分层抽样分配方案相比,该方案更加合理,工程适用性更高.     

基于使用方的火控系统的测试性研究

测试性研究已经成为火控系统发展的一个方向.本文分析了装甲装备火控系统可测试性和可维修性的现状,针对目前装甲装备火控系统的测试性与设计、制造分离的状态,提出必须实施系统的测试性设计.针对国内外装甲装备的测试性技术的发展和火控系统的复杂性及在装甲装备上的特殊地位,具体探讨了坦克火控系统的测试性指标和机内测试(BIT)技术的发展.     

测试系统成本控制与相对不确定度分配方法研究

根据动态规划原理，提出以控制成本为前提、利用计算机软件辅助计算对复杂测试系统的相对不确定度进行合理分配的方法。这种方法既能满足成本控制又能保证系统的相对不确定度指标的要求。     

复杂系统评估的综合层次分析法

针对现今多样的复杂系统评估问题，在原来传统层次分析法的基础上作了进一步的改进和扩展，形成了综合层次分析法——CAHP．该方法可运用于多类型属性并存的复杂系统评估中，并能对单个评价对象给出量化评估值．实践证明，该方法具有可行性和有效性、     

Using analytic hierarchy process to determine process economics in multivariate loss functions

Performance of a product usually depends on several responses (quality characteristics) which must meet all of the specifications simultaneously. This could be achieved by applying of (the) robust design methodology to problems with multiple characteristics. In the literature, several works have been published concerning multi-response optimization methods, which aim to achieve the best possible robustness. One of the approaches for multi-response optimization is Loss Function Approach which allows the practitioner to include variance–covariance structure of the responses, prediction quality and the economic importance of the responses relevant to the product or process. In this paper, we propose utilizing Analytic Hierarchy Process, a multi-criteria decision making tool, to determine the economic importance matrix in the multivariate loss function. An example of the suggested method is presented on a study conducted for a company producing water proof polymer roofing materials.     

The analytic hierarchy process applied to maintenance strategy selection

This paper describes an application of the Analytic Hierarchy Process (AHP) for selecting the best maintenance strategy for an important Italian oil refinery (an Integrated Gasification and Combined Cycle plant). Five possible alternatives are considered: preventive, predictive, condition-based, corrective and opportunistic maintenance. The best maintenance policy must be selected for each facility of the plant (about 200 in total). The machines are clustered in three homogeneous groups after a criticality analysis based on internal procedures of the oil refinery. With AHP technique, several aspects, which characterise each of the above-mentioned maintenance strategies, are arranged in a hierarchic structure and evaluated using only a series of pairwise judgements. To improve the effectiveness of the methodology AHP is coupled with a sensitivity analysis.     

基于模糊层次分析的空间谱估计性能评价方法

在已经形成的多种空间谱估计方法中,为根据需求选择最优算法,建立了一种基于模糊层次分析的空间谱估计性能评价方法。从空间谱估计方法的处理性能和可实现性角度建立一种具有层次结构模型的性能评价准则,对各项性能评价指标做属性值规范化和权重系数模糊化处理,得到各评价指标的评价得分,累加得到处理算法性能量化评价的综合判决结果。基于仿真试验和某次海试试验结果,对常规波束形成(Conventional Beam Forming,CBF)、最小方差无畸变响应(Minimum Variance Distortion Response,MVDR)、多信号分类(Multiple SIgnal Classification,MUSIC)三种空间谱估计方法进行评价,在较高输入信噪比和弱化计算量条件下,MUSIC为评价最优算法。试验结果表明,此评价方法可以按需求实现最优空间谱估计方法的选择。     

A method for risk-informed safety significance categorization using the analytic hierarchy process and bayesian belief networks

A risk-informed safety significance categorization (RISSC) is to categorize structures, systems, or components (SSCs) of a nuclear power plant (NPP) into two or more groups, according to their safety significance using both probabilistic and deterministic insights. In the conventional methods for the RISSC, the SSCs are quantitatively categorized according to their importance measures for the initial categorization. The final decisions (categorizations) of SSCs, however, are qualitatively made by an expert panel through discussions and adjustments of opinions by using the probabilistic insights compiled in the initial categorization process and combining the probabilistic insights with the deterministic insights. Therefore, owing to the qualitative and linear decision-making process, the conventional methods have the demerits as follows: (1) they are very costly in terms of time and labor, (2) it is not easy to reach the final decision, when the opinions of the experts are in conflict and (3) they have an overlapping process due to the linear paradigm (the categorization is performed twice—first, by the engineers who propose the method, and second, by the expert panel). In this work, a method for RISSC using the analytic hierarchy process (AHP) and bayesian belief networks (BBN) is proposed to overcome the demerits of the conventional methods and to effectively arrive at a final decision (or categorization). By using the AHP and BBN, the expert panel takes part in the early stage of the categorization (that is, the quantification process) and the safety significance based on both probabilistic and deterministic insights is quantified. According to that safety significance, SSCs are quantitatively categorized into three categories such as high safety significant category (Hi), potentially safety significant category (Po), or low safety significant category (Lo). The proposed method was applied to the components such as CC-V073, CV-V530, and SI-V644 in Ulchin Unit 3 NPP in South Korea. The expert panel consisted of two probabilistic safety assessments (PSA) experts and one system design expert. Before categorizing the components, the design basis functions, simplified P and IDs, and the Fussell-Vesely (FV) importance and the Risk Achievement Worth (RAW) of the PSA were prepared for the experts' evaluations. By using this method, we could categorize the components quantitatively on the basis of experts' knowledge and experience in an early stage.     

基于模糊层次分析的相控阵无损检测仪器可靠性分配方法

层次分析法一定程度受到主观因素影响,该文综合层次分析法与三角模糊数方法提出一种基于模糊层次分析的相控阵无损检测仪器可靠性分配方法。通过引入模糊参数降低可靠性分配决策过程的主观性,以熵最小原则作为参数选择依据,使方法能够更充分、客观、综合地反映专家意见。应用表明,相控阵无损检测仪器主机可靠性分配结果有效、可行,具有一定的工程应用价值。     

A note on the application of the data envelopment analytic hierarchy process for supplier selection

In a very recent paper by Sevkli et al. (Sevkli, M., Koh, S.C.L., Zaim, S., Demirbag, M. and Tatoglu, E., 2007. An application of data envelopment analytic hierarchy process for supplier selection: a case study of BEKO in Turkey. International Journal of Production Research, 45 (9), 1973–2003), the data envelopment analytic hierarchy process (DEAHP) was claimed to outperform the analytic hierarchy process (AHP) method and provide a better decision for supplier selection. This note illustrates the weaknesses of the DEAHP, demonstrating the invalidity of the above claim. The case study of BEKO in Turkey is re-examined and shows that extra verification is needed to come to a decision.     

Pressure analysis for green supply chain management implementation in Indian industries using analytic hierarchy process

Due to growing awareness about environment protection, firms are forced to implement environmental practices to enhance their green image. In recent times, academicians and practitioners have shown interest in green marketing and green supply chain management (GSCM). Fields of green branding and sustainability have seen special interest from different business disciplines including information management, marketing, supply chain management, etc. Due to economical and ecological impact, there is a growing concern for the environment and related critical issues. Pressure on the environment is dynamic and diverse, and demands new levels of accountability, financial commitment and supply chain capabilities. Indian manufacturing industries have started adopting green concepts in their supply chain management giving special attention to environmental issues based on pressures from different directions, e.g. customer pressure, government regulations etc. Yet, industries struggle hard to identify essential pressures for implementation of GSCM. This work focuses mainly on identifying such pressures for implementation of GSCM. Initially 65 pressures were identified through detailed literature and categorised into six groups. Then common acceptable pressures were identified through a questionnaire survey from different industrial sectors in Phase 1. Finally, essential pressures are prioritised with the help of analytic hierarchy process in Phase 2.     

A supplier selection model: a comparison of fuzzy logic and the analytic hierarchy process

This paper aims to compare two tools for decision makers that intend to support the decision of the selection of the appropriate supplier. Suppliers are crucial to both the efficiency and effectiveness of the performance of companies. A critical success factor of these companies is the selection of the appropriate supplier. A methodology is proposed to optimise the evaluation process based on different criteria. The proposed approach extends the one proposed by Ordoobadi (2009 Ordoobadi, SM. 2009. Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14(4): 314–327. [Crossref], [Web of Science ®] , [Google Scholar], Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14 (4), 314–327) who proposed the application of fuzzy logic (FL) where we use the same example case study in order to compare the analytic hierarch process (AHP) with FL. In this paper we demonstrate how we can achieve the same objective of expressing human assessments in the form of linguistic expressions by using AHP. Moreover, we demonstrate the capability to run a sensitivity analysis which helps to understand the causal relationships among the different factors. We demonstrate how this capability can help us to explain and predict the different relationships among criteria and alternatives. Moreover, we provide a measure that is able to capture the consistency of the decision maker's preferences. In our approach we provide a single unit of scale that is not only capable of ranking suppliers but also provides an understanding of the difference in scale between different suppliers which can then help to allocate resources accordingly. These facilities are not offered by Ordoobadi (2009 Ordoobadi, SM. 2009. Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14(4): 314–327. [Crossref], [Web of Science ®] , [Google Scholar]). The proposed approach here can help companies to identify the best supplier in changing environments. The paper describes a decision model that incorporates a decision maker's subjective assessments and applies a multiple criteria decision making technique to manipulate and quantify these assessments. Unlike many similar studies, two techniques have been performed on the same case study in order to improve our understanding of the differences in the proposed techniques.