多指标选优的极差分调法

在很多试验或计算性项目中,常常要考核多个指标,解决多指标选优问题的有效方法之一是综合评分法,我国在这方面己有很好的论述并取得了大量应用成果 (参见[2])。本文提出的极差分调法与综合评分法互为补充.采用极差分调法可在选优的不同阶段灵活地利用各指标极差分析所提供的信息,不断调整选优方向,特别在指标个数不算太多,指标间又有某种相关关系时,使用这种方法显得更为灵活、方便.以下用窄带有源滤波器的选优问题(参见[1]为例介绍这种选优方法. 一、用极差分调法进行参数选优 中心频率为f0=1000Hz的四级八阶有源滤波器的相对衰减函数为(…     

指派问题在供应商选优决策中的应用

通常供应链中供应商选优问题为多指标决策问题,本将此问题视为指派问题。指派问题中的关键是确定“效率”矩阵,本充分利用供应商单排序结果,评价指标权重以及供应商指标评价值构造了“效率”矩阵,建立了供应商综合选优指派问题模型,案例试算表明该方法合理、有效,为多指标方案决策提供了又一种可行的决策方法。     

M×N不同顺序排序的图表作业法

<正> M×N同顺序的排序选优已有了比较理想的解决方法,但对M>3不同顺序的排序选优,至今尚无理想的方法。特别是在多品种小批单个生产中,工序的顺序随着零件不一致,而顺序也不规则。要想确定产品及零件加工的理想顺序,就要做到既要保证总的生产周期和产品     

两两比较法及其在社会体育专业学生素质评价中的应用

研究了两两比较法并应用数理统计的理论研究了综合评价中的统计数量化的方法,使得多因素综合选优问题变为比较清晰、简单、又具有较强的区分力.     

求解群体多目标最优化问题的联合有效数法

群体多目标最优化是群体决策和多目标最优化相交叉的一个边缘研究领域,其主要特点是对由多个决策者提供的具多个目标的最优化问题,进行定量和定性相结合的方案选优或决策排序.因此,它的理论和方法在现代社会的重大决策中有着广阔的应用前景.     

群众决策的ακ-较多规则

对于群体决策问题,本文给出一个新的带参数的ακ-较多规则,并且讨论了在此规则下个体选优解和群体选优解的关系。此外,还论证了相应的ακ-较多规则满足Arrow公理的情况。     

群众决策的ak—较多规则

对于群体决策问题，本文给出一个新的带参数的ａｋ－较多规则，并且讨论了在此规则下个体选优解和群体选优解的关系，此外，还论证了相应的ａｋ－较多规则满足Ａｒｒｏｗ公理的情况。     

层次分析法在城市中小学建筑设计方案评价选优工作中的应用

<正> 一、引言众所周知,建筑设计方案的质量对建筑产品起着关键作用。因此,对建筑设计方案的评价选优无疑是非常重要的工作。笔者为此而建立了一个多目标决策系统,可用于进行城市中小学建筑设计方案(简称为     

大系统试验选优理论研究

针对某些高维动态、大型线性、复杂非线性、复杂模拟仿真和定性知识模型，该文全面总结了作者采用数学模型、知识模型和试验选优理论相结合的方法而提出的试验选优理论，该理论体系可以使一些目前无法求解或很难求解的大型问题获得近似解。并在复杂水资源系统的优化中取得了良好的效果。     

R-PVC挤出粒料优化设计——介绍多指标试验设计一种方法

用优化理论指导生产试验获得的最佳方案，应该使产品质量和加工性能都好且原料成本低．本文介绍的方案特点之一是在试验设计过程中将三者紧密地联系在一起进行优选．同时，对多指标且需保证各项指标特性值应满足一定要求的试验设计，用多指标综合评分法就难于兼顾，本文介绍一种选优方法．结果，只经两轮试验就获得最佳方案，经济效应显著．     

A parameterized ordering for cache-, register- and pipeline-efficient Givens QR decomposition

A parameterized ordering of Givens rotations and guidelines for choosing parameter values is presented in the context of QR decomposition. Although a standard selection of parameter values retrieves an ordering that corresponds to a well-known algorithm, we show that non-standard values decrease the execution time. We implement the new ordering on an Intel Pentium Pro system, a single thin POWER2 processor of the IBM SP2, and a single R8000 processor of the SGI POWER Challenge XL. On each machine, we observe performance that is more than twice that of the original ordering. This revised version was published online in June 2006 with corrections to the Cover Date.     

Towards a Tighter Coupling of Bottom-Up and Top-Down Sparse Matrix Ordering Methods

Most state-of-the-art ordering schemes for sparse matrices are a hybrid of a bottom-up method such as minimum degree and a top-down scheme such as George's nested dissection. In this paper we present an ordering algorithm that achieves a tighter coupling of bottom-up and top-down methods. In our methodology vertex separators are interpreted as the boundaries of the remaining elements in an unfinished bottom-up ordering. As a consequence, we are using bottom-up techniques such as quotient graphs and special node selection strategies for the construction of vertex separators. Once all separators have been found, we are using them as a skeleton for the computation of several bottom-up orderings. Experimental results show that the orderings obtained by our scheme are in general better than those obtained by other ordering codes.This revised version was published online in October 2005 with corrections to the Cover Date.     

A Preference Order Dynamic Program for a Knapsack Problem with Stochastic Rewards

In this paper, we extend the knapsack problem to include more realistic situations by treating the rewards (or values) associated with each item included in the solution as random variables with distributions that are known (or may be estimated) rather than known integers, as in the usual formulation. We propose a dynamic programming solution methodology where the usual real-valued return function is replaced by a preference ordering on the distributions of returns from the items selected. In addition to extending previous solutions to the knapsack problem, we demonstrate the selection of a preference ordering criterion and illustrate the conditions required of the ordering to guarantee optimality of the procedure. A sample problem is shown to demonstrate the algorithm, and results of computational experience with 459 problems of varying sizes and parameters are presented.     

Lexicodes over finite principal ideal rings

Let R be a (possibly noncommutative) finite principal ideal ring. Via a total ordering of the ring elements and an ordered basis a lexicographic ordering of the module \(R^n\) is produced. This is used to set up a greedy algorithm that selects vectors for which all linear combinations with the previously selected vectors satisfy a pre-specified selection property and updates the to-be-constructed code to the linear hull of the vectors selected so far. The output is called a lexicode. This process was discussed earlier in the literature for fields and chain rings. In this paper we investigate the properties of such lexicodes over finite principal ideal rings and show that the total ordering of the ring elements has to respect containment of ideals for the algorithm to produce meaningful results. Only then it is guaranteed that the algorithm is exhaustive and thus produces codes that are maximal with respect to inclusion. It is further illustrated that the output of the algorithm heavily depends on the total ordering and chosen basis.     

Sequencing a Set of Alternatives Under Time Constraints

We consider a problem of sequencing a set of alternatives (i.e. manufacturing methods, job applicants or target journals) available for selection to complete a project. Associated with each alternative are the probability of successful completion, the completion time, and the reward obtained upon successfully completing the alternative. The optimal sequencing strategy that maximizes the expected present value of total rewards, is derived based on a simple ordering parameter. We further consider an extension in which one of the alternatives will not be available for selection if not selected by a certain time, and another extension in which the selection process is allowed only for a limited period of time. We propose solution strategies to the selection and sequencing problem under time constraints.     

一个组面试问题的最佳顺序安排

采用边界阶段方法讨论最优组面试问题的最佳划分选择策略,在允许调整面试顺序的情况下,得到最优组面试问题中如何重新安排面试顺序能够以最大概率录用到最优应聘者的排序策略.     

Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming

The aim of this paper is to solve a supplier selection problem under multi-price level and multi-product using interactive two-phase fuzzy multi-objective linear programming (FMOLP) model. The proposed model attempts to simultaneously minimize total purchasing and ordering costs, a number of defective units, and late delivered units ordered from suppliers. The piecewise linear membership functions are applied to represent the decision maker’s fuzzy goals for the supplier selection and order allocation problem, and can be resulted in more flexibility via an interactive decision-making process. To demonstrate effectiveness of the proposed model, results of applying the proposed model are shown by a numerical example. The analytical results show that the proposed approach is effective in uncertain environments and provide a reliable decision tool for integrated multi-objective supplier selection problems.     

An MCDM model for media selection in the dual consumer/industrial market

An optimal advertising media selection is a strategic factor for the operations of both traditional and e-business environments. This paper presents a case study that considers two options: industrial and consumer products. In order to resolve the strategic decision-making about dual market high technology products, a mixed integer goal programming model is developed to facilitate the advertising media selection process.Preemptive priority ordering is established by the analytic hierarchy process. The problem is solved lexicographically using a software package, based on the data obtained from the e-business industry in Korea. The satisfying solution is identified and analyzed, and sensitivity analyses are performed for model flexibility.     

On the impact of conditional expectation estimators in portfolio theory

In this paper, we investigate the implications for portfolio theory of using conditional expectation estimators. First, we focus on the approximation of the conditional expectation within large-scale portfolio selection problems. In this context, we propose a new consistent multivariate kernel estimator to approximate the conditional expectation and it optimizes the bandwidth selection of kernel-type estimators. Second, we deal with the portfolio selection problem from the point of view of different non-satiable investors, namely risk-averse and risk-seeker investors. In particular, using a well-known ordering classification, we first identify different definitions of returns based on the investors preferences. Finally, for each problem, we examine several admissible portfolio optimization problems applied to the US stock market. The proposed empirical analysis allows us to evaluate the impact of the conditional expectation estimators in portfolio theory.     

The efficient frontier for bounded assets

This paper develops a closed form solution of the mean-variance portfolio selection problem for uncorrelated and bounded assets when an additional technical assumption is satisfied. Although the assumption of uncorrelated assets is unduly restrictive, the explicit determination of the efficient asset holdings in the presence of bound constraints gives insight into the nature of the efficient frontier. The mean-variance portfolio selection problem considered here deals with the budget constraint and lower bounds or the budget constraint and upper bounds. For the mean-variance portfolio selection problem dealing with lower bounds the closed form solution is derived for two cases: a universe of only risky assets and a universe of risky assets plus an additional asset which is risk free. For the mean-variance portfolio selection problem dealing with upper bounds, the results presented are for a universe consisting only of risky assets. In each case, the order in which the assets are driven to their bounds depends on the ordering of their expected returns.