线性分配方法的逆序问题研究

指出了线性分配方法的决策结果可能产生逆序,然后提出排序向量和相对分配比 的 概念,以分析线性分配方法决策结果产生逆序的原因,并在此基础上构建顺序线性分配方法 ,最后通过算例验证顺序线性分配方法决策结果的保序性。     

有限单元系统的结构组合优化设计

本文提出了由有限元系统组成的结构组合优化设计的多阶段决策算子方法;以含梁单元的平面刚架结构与弹性薄板结构组合优化设计为例说明了方法的应用。由于这个方法具有通用性好、公式推导和程序简单,节省计算机存储等优点,它可在工程设计领域中广泛应用。     

总承包工程PDMs决策模型研究与应用

针对传统模型未考虑决策者在决策问题上的不确定性因素而可能导致方案排序不稳定的问题,应用不确定多属性(UMA)理论对总承包工程交易模式(PDMs)决策进行了研究。根据总承包工程交易模式的不确定性因素提出了模型假设条件,选择了模型构建的不确定多属性理论工具,应用UEOWA算子和ULHA算子构建了基于可能度矩阵排序向量的决策支持模型;建立了我国总承包工程交易模式决策问题的属性评价体系,通过工程实例说明了模型的决策步骤和方法。     

四探针电阻率微区测量改进的Rymaszewski法厚度修正

为了解决四探针技术在微区测量中不能测试较厚样品的问题，提出了一种厚度修正方法该方法基于镜像源理论，以样品的上下边界为镜面，反复镜像电流源得到无限系列镜像源．将这些镜像源所产生的电位对测试点的影响进行叠加，推导出利用改进的Rymaszewski方形四探针法进行电阻率测量时的厚度修正公式，又利用规范化拟合法得到了多项式．修正后的结果反映了样品的真实电阻率，实验结果验证了该修正公式的正确性，从而完善了改进的Rymaszewski方形四探针的微区测量方法，使该方法能够应用到实际的测量中，提高了其实用性．     

同时计算实序列的DFT和IDFT的新公式及其在数字音频水印中的应用

为了提高数字音频水印中的水印嵌入效率，利用同时计算N点实序列的离散傅立叶变换（DFT）和逆离散傅立叶变换（IDFT）的新公式提出了一种改进的数字音频水印嵌入方案。通过对D17F的一些性质的分析，利用DFF的对称性，给出了同时实序列的DFT和IDFT的新公式，并改正了Gunther相关公式中的少数错误。在此基础上，将新公式应用到数字音频水印的嵌入算法中，通过实验表明了新方案大约可提高30％的水印嵌入性能。     

边缘检测算法在污损条码识别中的应用研究

针对污损条码识别速度慢和正确识别率低的问题,提出了利用图像边缘检测算子来提取污损条码的边缘特征并进行识别。分析了常用边缘检测算法的算子特征后,采用Sobel算子进行了条码的边缘检测。针对原算子的不足,提出了一种改进的Sobel算子,改进算子增加2个方向的运算模版,能克服原算子在边缘提取时图像线条较粗等缺陷。仿真实验表明,改进的算子能快速有效地识别污损条码,正确率高达98.5%。     

单件小批条件下的动态生产函数模型

基于微观投入产出理论，本文总结了几种动态生产函数，并对其在单件小批订货生产类型企业的生产决策中的应用条件进行了研究。提出了改进基本模型的具体方案。     

改进的混合粒子群优化算法

针对粒子群算法后期收敛速度较慢,易陷入局部最优的缺点,提出了改进的混合粒子群算法.通过更改现有的速度更新公式,加入扰动项,以及引入交叉和变异算子等措施,改进了粒子群算法的性能.数值试验表明,改进后的粒子群算法在全局寻优和局部寻优能力上均得到提高,是一种有效的优化算法.     

三角法激光测量系统的误差分析及消除方法

从理论和应用两方面分析了三角法激光测量系统的误差，得出：定点标定和插值可以消除光学放大率随测量点位置变化的影响；双光路方案或标准点校准方法可以消除光线路径随环境温度，湿度变化而引入的误差；提高激光束质量和采用峰值求光斑中心的方法，可以消除激光束强度分布，物体表面粗糙度，CCD传感器，信号处理电路等引入的误差。     

基于发电运营辅助决策模型的遗传算法研究

本研究应用遗传算法优化发电运营辅助决策模型，并利用仿真实验证明了遗传算法应用于发电运营辅助决策模型时的最优算子配置建议，研究结果表明，遗传算法在处理离散，非连续模型方面性能优良，收敛速度快，具备全局寻优能力，同时，利用遗传算法进行发电运营辅助决策的模型优化，可以让模型在实际应用中的更优良、更简单和更可靠。     

Evaluating Relationship of Consistency Ratio and Number of Alternatives on Rank Reversal in the AHP

This article shows the simulation result of the rank reversal phenomenon with respect to the changing values of consistency ratio and the number of alternatives. The simulation result reveals that the number of alternatives and the consistency ratio have significant effect on the occurrence of rank reversal. The more the number of alternatives is, the higher the probability of rank reversal occurrence becomes. The more inconsistent the judgment is, the more likely the rank reversal will occur. This result is based on two-way analysis of variance in which the number of alternatives and the consistency ratio are considered as factors, and the probability of rank reversal occurrence is regarded as the response variable. The number of alternatives of 4 to 7 and the consistency ratios ranging between 0.02 and 0.20 are investigated. The observation is based on the rank reversal that occurs after adding a copy of the best existing alternative.     

Modified analytic hierarchy process to incorporate uncertainty and managerial aspects

The analytic hierarchy process (AHP) is a powerful multiple-criteria decision analysis technique for dealing with complex problems. Traditional AHP forces decision-makers to converge vague judgements to single numeric preferences in order to estimate the pairwise comparisons of all pairs of objectives and decision alternatives required in the AHP. The resultant rankings of alternatives cannot be tested for statistical significance and it lacks a systematic approach that addresses managerial/soft aspects. To overcome the above limitations, the present paper presents a modified analytic hierarchy process, which incorporates probabilistic distributions to include uncertainty in the judgements. The vector of priorities is calculated using Monte Carlo simulation. The final rankings are analysed for rank reversal using analysis of variance, and managerial aspects (stake holder analysis, soft system methods, etc.) are introduced systematically. The focus is on the actual methodology of the modified analytic hierarchy process, which is illustrated by a brief account of a case study.     

Rank ordering engineering designs: pairwise comparison charts and Borda counts

Designers routinely rank alternatives in a variety of settings using a staple of comparison, the pairwise comparison. In recent years questions have been raised about the use of such comparisons as a means of calculating and aggregating meaningful preference or choice data. Results on voting have been used to argue that the positional procedure known as the Borda count is the best pairwise voting procedure, or at least the only one that is not subject to a number of demonstrable problems. We show here that pairwise comparison charts (PCC) provide results that are identical to those obtained by the Borda count, and that the PCC is thus not subject to the arguments used against non-Borda count methods. Arrow's impossibility theorem has also been invoked to cast doubt upon any pairwise procedure, including the Borda count. We discuss the relevance of the Arrow property that is lost in the Borda count, the independence of irrelevant alternatives (IIA). While the theoretical consequences of IIA are devastating, it is not clear that the same is true of its practical consequences. Examples are presented to illustrate some primary objections to pairwise methods.     

Learning non-monotonic additive value functions for multicriteria decision making

Multiattribute additive value functions constitute an important class of models for multicriteria decision making. Such models are often used to rank a set of alternatives or to classify them into pre-defined groups. Preference disaggregation techniques have been used to construct additive value models using linear programming techniques based on the assumption of monotonic preferences. This paper presents a methodology to construct non-monotonic value function models, using an evolutionary optimization approach. The methodology is implemented for the construction of multicriteria models that can be used to classify the alternatives in pre-defined groups, with an application to credit rating.     

模糊性偏序关系上的信息融合

用三角模引进模糊性偏序关系，采用算法平均，极大值，极小值融合算子，用超过关系和非不协调关系对多准则评估信息进行融合和分类，证明了这种方法符合阿罗公共选择的一些性和非独裁性条件，但不符合无关备选对象独立性，指出了某些作者关于独立性结论的错误。     

Group-based product scheme-screening decision method based on fuzzy AHP and evidential reasoning theory

Selecting the favourable product scheme is the first step to successful new product development (NPD). There are usually large numbers of uncertainties in product scheme evaluation and screening process of NPD due to lack of or incomplete reliable information. Considering fully the uncertainties and then conducting correct reasoning could guarantee reliability and rationality of scheme-screening results. As an extension of analytic hierarchy process (AHP), fuzzy AHP inherits multi-merits of the AHP approach and is capable of dealing with fuzzy information effectively, but it still has two weaknesses. One is the well-known ranking reversal problem. Although several researchers have analysed the reasons, we think the root cause for ranking reversal problem is due to the fact that AHP treats weights of attribute criteria and performance scores of alternatives in the same way. Therefore, we intend to deal with attribute weights and performance scores of alternatives separately and introduce evidential reasoning (ER) theory, which is good at uncertain reasoning, into fuzzy AHP to calculate the performance scores of alternatives. On the other hand, in view of the difficulty in resolution for fuzzy weights from fuzzy comparison matrix, a linear goal-programming model is proposed to calculate fuzzy weights, whose objective is to minimise the inconsistency degree of comparison matrix. By combining fuzzy AHP with ER, a group-based hybrid decision model FAHP-ER is developed. The hybrid model not only gets a great improvement in the capability of dealing with uncertainty, but also reflects the most real decision scenario and thinking process of the decision maker. Finally, a case study for schemes screening of the rotor and bearing system in the turbine generator is presented to demonstrate the application of the hybrid decision method.     

Rank formulas for certain products of matrices

For two matrix operations, calledquasi-direct sum andquasi-outer product, we determine their deviations from multiplicative behaviour of the rank. The second operation arises in the determination of the function table for so-called sum-type functions such as the Hamming distance. A consequence of the corresponding rank formula is, that the frequently used log rank can be a very poor bound for two-way communication complexity. Instead, as was shown in [9], a certainexponential rank gives often excellent or even optimal bounds.     

The chemical rank estimation for excitation-emission matrix fluorescence data by region-based moving window subspace projection technique and Monte Carlo simulation

A novel method, region-based on moving window subspace projection technique (RMWSPT) coupled with Monte Carlo simulation, was developed for the chemical rank estimation of excitation-emission matrix (EEM) fluorescence data arrays. RMWSPT determines the chemical rank by performing singular value decomposition (SVD) on the unfolded matrices of original data array and the subarrays yielded by a moving window, and through employing the subspace projection technique on the difference between the corresponding sub-bands of the significant eigenvectors and those of subarrays. Compared with the traditional methods, it utilizes the information from eigenvectors combined with the projection residual sum of squares values to estimate the rank of the EEM data arrays instead of using the eigenvalues. Two simulated and two real EEM data arrays were analyzed to demonstrate the excellent performance of the RMWSPT. Moreover, its performance was compared with that of other three factor-determining methods, i.e., factor indicator function (IND), the core consistency diagnostic (CORCONDIA) test and two-mode subspace comparison (TMSC) approaches. The results showed that the newly proposed method can accurately and quickly determine the chemical rank to fit the trilinear model, and it can deal with more complex situations in the presence of severe collinearity and trace concentration. The RMWSPT method thus lights a new avenue to determine the chemical rank of EEM data arrays and may hold great potential to be extended as a promising alternative for the chemical rank estimation.     

Materials selection using complex proportional assessment and evaluation of mixed data methods

Material selection is a very fast growing multi-criteria decision-making (MCDM) problem involving a large number of factors influencing the selection process. Proper choice of material is a critical issue for the success and competitiveness of the manufacturing organizations in the global market. Selection of the most appropriate material for a particular engineering application is a time consuming and expensive process where several candidate materials available in the market are taken into consideration as the tentative alternatives. Although a large number of mathematical approaches is now available to evaluate, select and rank the alternative materials for a given engineering application, this paper explores the applicability and capability of two almost new MCDM methods, i.e. complex proportional assessment (COPRAS) and evaluation of mixed data (EVAMIX) methods for materials selection. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two illustrative examples are cited which prove that these two MCDM methods can be effectively applied to solve the real time material selection problems. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained which almost match with the rankings as derived by the past researchers.