考虑不完全语义信息的风险型供应商选择方法

考虑供应商选择中决策者的风险态度,提出了一种考虑不完全语义决策信息的风险型供应商选择方法。结合二元语义模型和D数获取并处理决策者给出的不确定性和不完全的供应商评价信息,基于D数信息融合模型获取集结后的群决策信息。基于累积前景理论对候选供应商进行评价,考虑市场竞争获取前景价值函数,基于综合前景值最大化优化模型获得的指标权重对供应商进行排序。最后以挖掘机救援与现场维修服务供应商的评价与选择为例,验证了所提方法的有效性。     

基于AHP-熵权的绿色包装评价指标重要度分析

目的 提供一种基于AHP（层次分析法）和熵权定量化的绿色包装评价指标重要度确定方法。方法 结合现有资料,从环境、资源、经济、功能等4个维度构建多层次绿色包装评价指标体系,运用AHP确定各个指标的主观权重,并利用熵权法获得各项指标的客观权重;最后,运用乘法组合赋权原理将两者整合,得到各项指标的综合重要度。结果 定量化的重要度标定实现了绿色包装评价指标的有效排序,得出绿色包装一级评价指标的重要度排序为资源指标>环境指标>经济指标>功能指标,二级指标重要度的前3位是空位容积率、原材料中的有害成分、包装成本。结论 综合AHP权重值与熵权权重值,评价指标的专家主观偏好与客观差异得到了更好的融合,获得了更为合理的指标权重,该方法能够为绿色包装评价体系的制定提供有效参考。     

TOPSIS法在供应商评估中的应用

主要介绍了逼近理解排序法的方法,同时利用熵值法确定评价指标的权重,并详细描述了该方法在供应商评估中的应用。     

供应商选择的三维动态组合评价方法及应用

建立了一种供应商选择和评价的动态组合评价模型。在指标权重的确定中以G1法确定主观权重,熵值法确定客观权重。将主、客观方法得到的权重组合集成,避免了单一评价方法易产生的片面性结果;对指标评估值采用动态的分阶段线性组合,充分考虑了被评价对象在不同时期的客观状况对评价结果的影响,弥补了只考虑当前状况易产生的偶然性结果。最后用实例说明了方法的有效性。     

三端点区间数互反判断矩阵的排序方法研究

本文研究了三端点区间数互反判断矩阵的一致性和排序方法。在三端点区间数互反判断矩阵完全一致性概念的基础上,首次将矩阵特征向量思想引入三端点区间数互反判断矩阵之中——研究了三端点区间数互反判断矩阵的一致性与权重向量之间的类似特征向量关系,弊运用线性规划模型给出一种处理三端点区间数决策者对方案属性权重的方法,然后通过三端点区间数权重向量的期望值,进行方案集结排序。最后通过具体的案例,验证了所提出方法的有效性和适用性。     

基于TAME-EDKT模型的适老化家电产品设计评价

目的 为解决适老化家电产品设计评价过程中指标集确定的非适老性、指标间相互作用关系的干扰性和权重值确定过程中忽略用户需求的片面性等问题,基于老年技术接受模型,提出一种综合评价模型方法。方法 首先,基于老年技术接受模型确定设计方案评价指标;其次,基于熵权法确定评价指标的初始权重,运用DEMATEL法确定指标之间的相互作用权重,以KANO模型进行权重调整,确定最终的指标权重,并基于TOPSIS法进行方案排序优选;最后,运用该方法进行适老化吸尘器设计方案优选,并进行对比验证。结论 该方法可有效确定适老化家电产品设计评价指标集的适老性,较好地考虑了指标间的相互作用关系及用户需求对指标权重的影响,并进一步完善适老化家电产品设计领域的理论研究。     

重视熵权的供应商选择DEA/TOPSIS联合方法

通过分析和总结供应链环境下战略供应商评价选择的准则、指标和方法,以供应链核心企业选择战略性供应商为目标,提出了战略性供应商评价的指标体系.研究了一种新的DEA模型,首先利用熵权法为各指标确定一个权重,然后利用线性加权方法将其与C2R模型的自由权重合成生成综合权重,并使用DEA/TOPSIS联合方法解决供应商选择问题,克服了DEA法与TOPSIS法单独应用时各自存在的不足.实证分析证明了该模型及方法的有效性,并为重视熵权的DEA/TOPSIS联合法在多目标规划中的运用提供了依据与参考.     

基于层次分析法的包装材料综合效益分析评价

研究建立了典型包装材料的综合效益评价指标体系,运用层次分析模型对其进行了评价和分析.通过问卷调查得到定性评价后,利用概率统计的原理对数据进行了处理,构造成对比较矩阵,并采用和积法计算最大特征根和特征向量.最终确定各影响因子的权重值,得到各影响因子的重要性排序.根据排序的结果和得到的权重值,对典型包装材料的综合效益做出了全面评价,并对几种典型材料的评价结果进行了分析,对包装材料企业发展方向的决策有一定的参考意义.     

基于隔栅-模糊 Borda 数的产品包装多视角评价模型

由于不同评价主体对包装设计方案评价要素的满意度或重视性排序有所差别,基于隔栅-模糊Borda数构建了包装方案的评价模型。首先建立了产品包装的评价指标体系,然后基于隔栅计算单一准则下的指标相对权重,再采用线性加权和法将底层指标的评价分值逐层向上级指标综合,直至得到设计方案的综合得分,实现了包装设计的多视角综合评价。最后,通过实例验证了模型的可操作性和有效性。     

供应商选择的区间层次分析法

对区间层次分析法(IAHP)研究中的关键即区间判断矩阵的权重计算及排序问题进行了研究,提出了一种有效算法,即通过将传统AHP中的排序方法进行区间扩展,构建了基于区间数运算法则的有效排序算法:方根法;并以供应商选择为例给予了具体说明,研究结果符合专家的不确定性思维判断.结论表明当矩阵阶数较少时,直接基于区间数运算法则的方根法是一种既简单又有效的算法.     

Visual signal detection in structured backgrounds. IV. Figures of merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses

Many investigators are currently developing models to predict human performance in detecting a signal embedded in complex backgrounds. A common figure of merit for model performance is d', an index of detectability that can be mathematically related to the proportion correct (Pc) when the responses of the model are Gaussian distributed and statistically independent. However, in many multiple-alternative forced-choice (MAFC) detection tasks, the target appears in one of M different locations within an image. If the image contains slow spatially varying luminance changes (low-pass noise), the pixel luminance values at the possible signal locations are correlated and therefore the model/human responses to the different locations might also be correlated. We investigate the effect of response correlations on model performance and compare different figures of merit for these conditions. Our results show that use of the standard d' index of detectability assuming statistical independence can lead to erroneous underestimates of Pc and misleading comparisons of models. We introduce a novel figure of merit d'(r) that takes into account response correlations and can be used to accurately estimate Pc. Furthermore, we show that d'(r) can be readily related to the standard index of detectability d' by d'(r) = d'/square root of (1 - r), where r is the correlation between the responses in any MAFC detection task. We illustrate the use of the theory by computing figures of merit for two linear models detecting a signal in one of four locations within medical image backgrounds.     

基于组合预测模型的铁路集装箱运量预测

为了克服单项预测模型的单一性和片面性等缺点,本文应用组合预测模型对铁路集装箱运量进行预测,以提高预测的准确性。通过对运量的历史数据分别采用多项式曲线模型和灰色预测模型建立单项预测模型,再利用径向基神经网络对两个单项预测模型的结果进行组合预测。研究结果表明,相比于两单项预测方法,组合预测方法所得运量的相对误差分别提高了3.19%和12.76%。最后,应用马尔科夫链模型对组合预测的结果进行分析和修正,增加预测结果的可靠性。     

Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

The analysis of many physical and engineering problems involves running complex computational models (simulation models, computer codes). With problems of this type, it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation, an improvement on existing methods for SA of complex computer models is described for use when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations, a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the uncertainty in an input. Most existing approaches to this problem either do not work well with a large number of input variables and/or they ignore the error involved in estimating a sensitivity index. Here, a new approach to sensitivity index estimation using meta-models and bootstrap confidence intervals is described that provides solutions to these drawbacks. Further, an efficient yet effective approach to incorporate this methodology into an actual SA is presented. Several simulated and real examples illustrate the utility of this approach. This framework can be extended to uncertainty analysis as well.     

Robust optimisation for risk-averse multi-period inventory decision with partial demand distribution information

We study robust multi-period inventory decisions for risk-averse managers with incomplete demand information for products with a short life cycle. The three inventory models we developed aim respectively to maximise expected profit, maximise conditional value-at-risk-based profit, and balance between the two objectives. We formulate each objective into an associated robust counterpart model under the assumption of ellipsoid distribution and again under the box distribution. The ellipsoid distribution-based robust model can be mathematically transformed into a non-linear programming which can be solved by finding solutions to some second-order cone programs, while the box distribution-based model can be converted into a general piecewise linear optimisation problem. We prove that the transformed versions are equivalent to the original ones and that both transformed models can be solved efficiently. Numerical examples are given to demonstrate the practicability of the proposed approach for dealing with uncertain demands. We find that the proposed optimisation approaches are robust under both the ellipsoid and box distributions. Finally, sensitivity analysis on the risk-averse degree and optimism index is conducted to validate the proposed models and solution approaches.     

Laplace moving average model for multi-axial responses in fatigue analysis of a cultivator

Modeling of loads on a vehicle through Laplace moving averages is extended to the multivariate setting and efficient methods of computing the damage indexes are discussed. Multivariate Laplace moving averages are used as statistical models of multi-axial loads represented by forces and moments measured at some locations of a cultivator. As opposed to models based on the Gaussian distribution, these models account explicitly for transients that have a common origin—vibrations that can be caused by large obstacles encountered by a cultivator or a vehicle driving into potholes. The model is characterized by a low number of parameters accounting for fundamental characteristics of multivariate signals: the covariance matrix representing size of loads and their mutual dependence, the excess kurtosis that in the model is related to relative size of transients, and the time scale that accounts for the vehicle speed. These parameters can be used to capture diversity of environmental conditions in which the vehicle operates. Distributions of parameter values that are specific to a given market or encountered by specific customers can be then used to describe the long term loading. The model is validated by analysis of the resulting damage index. It is shown that the parameters enter this index in a multiplicative and explicit manner and, for a given damage exponent, only the factor representing dependence on the kurtosis has to be obtained through regression approximation based on Monte Carlo simulations. An example of actual cultivator data is used to illustrate the accuracy of damage and fatigue life prediction.     

Ionization effect on arc plasma's optical diagnosis by the measurement of the refractive index

The effect of arc plasma ionization on its temperature diagnosis by the measurement of the refractive index is discussed. The refractive index of arc plasma in two conditions is compared: 1) only the first ionization is considered and 2) both the first and second ionizations are considered. In order to facilitate plasma temperature reconstruction, two corresponding refractive index models are deduced. For the sake of making this study universal, both the monatomic and dual-atomic molecule arc plasmas are chosen as typical examples for theoretical deduction and analysis. A condition, which can be adopted to estimate whether the second ionization should be considered in temperature reconstruction, is proposed. Finally, an argon arc plasma is chosen as an example for experiment, and the experimental results match well with the theoretical analysis. This study is crucial to arc plasma's optical diagnosis, which is based on the measurement of the refractive index.     

具有极点区域约束的广义模糊系统的输出反馈控制

针对模糊广义系统，将动态输出反馈控制器设计问题转化为一组矩阵不等式的可行解问题，使得闭环系统不仅具有鲁棒性能，而且能把闭环系统极点配置在一个给定的圆形区域内，从而保证系统具有期望的动态性能。     

Single-photon determination of transmission,index of refraction and material thickness

This paper describes how single photons of light can be used to determine the index of refraction and material thickness. The results of single-photon transmission and polarization interferometer experiments are analyzed to find the index of refraction and thickness of a microscope coverslip. The single photons were generated using non-collinear spontaneous down-conversion. The experimental results are in good agreement with derived theoretical models. This method of determining optical parameters at the quantum level may be useful in performing non-destructive measurements of light-sensitive samples with high precision.     

考虑非平稳因素的混凝土桥梁概率极限状态评估

为建立混凝土桥梁构件的概率极限状态评估方法,借助等超概率原则分析我国在役桥梁构件评估周期及评估基准期,引入个体风险准则、社会风险准则、生命质量指标及成本优化方法确定构件运营阶段目标可靠指标,分别考虑非平稳及平稳概率模型进行荷载效应及抗力评估值确定,基于可靠度理论开展运营阶段评估分项系数校准,并以一座在役桥梁为例进行算例分析。结果发现:考虑我国在役桥梁运维实际情况,构件评估周期、评估基准期可分别取为6年、10年;对于一级、二级、三级延性破坏构件,评估目标可靠指标分别建议为3.37、3.13及2.85;采用一般运行状态或密集运行状态下平稳车辆荷载效应模型进行评估时,评估标准值可分别取为设计汽车荷载效应的0.705倍及0.805倍,考虑非平稳车载过程进行评估时,可在连续非平稳过程离散化的基础上,引入动态广义极值模型确定评估基准期内荷载效应最大值分布,并以0.95分位值作为评估标准值;对于重要性等级为一级的延性构件,恒载效应及抗力评估分项系数分别建议为1.056与1.194,一般运行状态与密集运行状态汽车荷载效应评估分项系数建议值分别为1.081与1.054,研究成果可为现行桥梁构件安全评估方法修订提供参考。     

不等跨RC框架的抗连续倒塌理论分析及鲁棒性评判指标研究

现有钢筋混凝土(RC)框架抗连续倒塌的理论分析模型仅适用于等跨结构。通过理论推导将现有等跨结构的分析模型推广至不等跨结构,并提出了一个新的修正三折线模型,可以考虑不同工况下结构的抗弯、压拱、悬梁线和拉膜效应机制。利用所提理论分析模型建立了一个鲁棒性评判指标,用于危险工况的快速判断。数值与理论结果对比表明:所提出的修正三折线理论分析模型准确性高,能够为不等跨RC框架抗连续倒塌设计提供参考;所建立的鲁棒性评判指标可用于快速确定出RC框架的最危险柱子失效工况。