Choosing the Right Expert System Building Approach

The analytical hierarchy process (AHP) is used to systematically examine the relative appropriateness of contending expert system building approaches—algorithmic programming languages, symbolic programming languages, expert system shells, and knowledge engineering environments based upon characteristics of application domains and development environments. Two expert system projects, Back Expert, and R&D Expert, illustrate the value of this methodology. Domain and development environment characteristics identified in the AHP approach are then used to construct rule-based and Lotus 1-2-3 expert systems for selecting the right expert system building approach.     

A Three‐step Method for Choosing the Number of Bootstrap Repetitions

This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, confidence regions, hypothesis tests, p‐values, and bias correction. For each of these problems, the paper provides a three‐step method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval length, test's critical value, test's p‐value, or bias‐corrected estimate based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B=∞. The results apply quite generally to parametric, semiparametric, and nonparametric models with independent and dependent data. The results apply to the standard nonparametric iid bootstrap, moving block bootstraps for time series data, parametric and semiparametric bootstraps, and bootstraps for regression models based on bootstrapping residuals. Monte Carlo simulations show that the proposed methods work very well.     

PAHAP: A Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences

In this paper, we present a Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences (PAHAP), a new method for solving discrete alternative multicriteria decision problems. Following the Analytic Hierarchy Process (AHP), PAHAP uses pairwise preference judgments to assess the relative attractiveness of the alternatives. By first aggregating the pairwise judgment ratios of the alternatives across all criteria, and then synthesizing based on these aggregate measures, PAHAP determines overall ratio scale priorities and rankings of the alternatives which are not subject to rank reversal, provided that certain weak consistency requirements are satisfied. Hence, PAHAP can serve as a useful alternative to the original AHP if rank reversal is undesirable, for instance when the system is open and criterion scarcity does not affect the relative attractiveness of the alternatives. Moreover, the single matrix of pairwise aggregated ratings constructed in PAHAP provides useful insights into the decision maker's preference structure. PAHAP requires the same preference information as the original AHP (or, altematively, the same information as the Referenced AHP, if the criteria are compared based on average (total) value of the alternatives). As it is easier to implement and interpret than previously proposed variants of the conventional AHP which prevent rank reversal, PAHAP also appears attractive from a practitioner's viewpoint.     

Chainwise Paired Comparisons

The pairwise comparison technique is a building block of the Analytic Hierarchy Process (AHP), which has been popularly used for multicriteria decision analysis. This paper develops a shortcut technique in which only n paired comparisons forming a closed chain are needed for n decision elements. Together with the development of a simple and intuitive measure of (inconsistency, this technique derives the relative weights of decision elements via easy step-by-step calculations on a spreadsheet format. Its performance has been tested on Saaty's wealth of nations example. It is important to notice that ranking and weights yielded from this alternative technique are identical to Harker's incomplete pairwise comparison solution for the same chain orientation for the example tested.     

Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process*

In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it.     

Coordination in supply chains: an evaluation using fuzzy logic

Supply chain coordination (SCC) is an effective approach to improve supply chain (SC) performance. The coordination can be achieved when interdependent entities work together by sharing resources and information to achieve common objectives aligned to maximise customer value for the entire SC. There are a number of mechanisms by which the SC members can coordinate, e.g. contracts, information sharing, information technology and collaborative initiatives. To communicate frequently and effectively, the partners are required to have good information systems and capability to share information. To coordinate with each other the SC members are required to have capabilities to implement coordination mechanisms effectively. In this paper, a model is proposed to measure the effect of these mechanisms on the extent of coordination. Currently, there is no scale of measurement reported in the literature to measure the SCC. A fuzzy logic approach combined with the analytic hierarchy process (AHP) is proposed to evaluate the extent of coordination. The proposed methodology is demonstrated through a case study of an automotive parts manufacturer.     

A decision support system for the selection of a casting process using analytic hierarchy process

This paper presents an Analytic Hierarchy Process (AHP) based decision support system to select the most suitable casting process for a given product. The hierarchical structure of the proposed method allows the decision maker to compare the different casting processes using the material suitability and flexibility, geometrical complexity, dimensional tolerance and surface finish of the casting, and the cost as the criteria for selection. Judgemental inconsistency of the decision maker in selecting the casting process is taken care by ensuring that the value of consistency ratio is below (0.1). A numerical example is presented to illustrate the effectiveness of the proposed methodology for selecting the suitable casting process.     

基于层次分析法在价值链分析中的应用研究

竞争者价值链之间的差异是竞争优势的一个关键来源,但是竞争价值链分析大多停留在定性分析阶段,只是提出了分析思路和模式,在定量化分析方面尚未有较为深入的进展。层次分析法是解决这类问题的行之有效的方法,本文尝试将层次分析法引入价值链分析中,通过对不同企业价值链活动的分析,得出竞争优势的优先排序,作为企业并购的决策依据。研究表明,本方法对企业并购的决策分析是比较有意义的。     

具有不确定信息的风险型多目标决策理论及应用

具有不确定信息的多目标决策问题是一类常见的决策问题。给出该决策问题指标的分类方法和指标值的规格化公式,建立方案的距优距劣综合加权距离为最小的目标函数,从而得到一类求解这一问题的数学模型,并给出了一种目标权重的确定方法,很适用于实际问题,为解决不确定性系统的多目标决策问题提供了一种思路和方法。最后将其应用到水资源系统风险决策的一个实例中,表明了方法的可行性。     

Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression

We consider multiple criteria decision aiding in the case of interaction between criteria. In this case the usual weighted sum cannot be used to aggregate evaluations on different criteria and other value functions with a more complex formulation have to be considered. The Choquet integral is the most used technique and also the most widespread in the literature. However, the application of the Choquet integral presents two main problems being the necessity to determine the capacity, which is the function that assigns a weight not only to all single criteria but also to all subset of criteria, and the necessity to express on the same scale evaluations on different criteria. While with respect to the first problem we adopt the recently introduced Non-Additive Robust Ordinal Regression (NAROR) taking into account all the capacities compatible with the preference information provided by the DM, with respect to the second one we build the common scale for the considered criteria using the Analytic Hierarchy Process (AHP). We propose to use AHP on a set of reference points in the scale of each criterion and to use an interpolation to obtain the other values. This permits to reduce considerably the number of pairwise comparisons usually required by the DM when applying AHP. An illustrative example details the application of the proposed methodology.     

群体两两比较判断集成的最短距离方法

本文将Yu和Cook等提出的一般距离的概念应用到群体决策问题中,基于AHP原理,给出了群体决策集成问题的定义,综述了现有的两两比较判断集成方法,提出了一种基于最短距离的集成方法——最短距离方法(Mini-mum Distance Method,MSM),并用数值例示进行了说明,最后讨论了群体决策以及AHP未来可能的研究课题。     

基于流形学习的非一致性判断矩阵排序方法

针对传统层次分析法(AHP)在构造判断矩阵过程中需要满足一致性条件问题,本文研究AHP方法需要进行一致性调整的原因,提出了一种基于流形学习的非一致性判断矩阵排序方法。在非一致性判断矩阵排序过程中,首先基于近邻距离的概念,构建出判断矩阵所对应数据集的近邻距离矩阵;然后以近邻点的线性表示为基础,将每个数据点映射到一个全局低维坐标系,并据此获得判断矩阵所对应的低维嵌入;根据各层求解出的低维嵌入对各层要素进行优劣排序,进而得到最终排序结论。最后,通过数值案例验证了所提方法的有效性和实用性。     

基于SVM求解不均衡数据集分类的主观权重约束方法

支持向量机(SVM)的二类分问题中针对不平衡数据集可以通过减少样本信息的不对称性和改进算法来解决。本文针对中小企业中有财务风险与无财务风险样本的不平衡性问题,使用一种带有主观权重约束条件的支持向量机新模型对样本进行分类。实验表明新模型确实提高了财务风险企业即少类样本的识别性能,是一种类别不均衡学习(class imbalance learning)的新方法。     

基于变精度粗集的判断矩阵构造方法

判断矩阵的一致性条件,是用AHP法进行排序的必要条件。由于专家的主观性,判断矩阵一般不能满足一致性条件。本文利用变精度粗集的依赖度因子,给出了一种判断矩阵的构造方法。这种方法充分利用排序方案因素的原始信息,又综合了多专家的判断。     

A BETTER ADDITIVE CONGRUENTIAL RANDOM NUMBER GENERATOR?

Roger L. Burford reported on a new additive congruential random number generator in the April, 1973 issue of Decision Sciences [1]. Burford claimed a number of advantages for the new generator, including a faster generation time than the commonly used multiplicative generators. This note describes an extensive examination of the Burford generator in comparison with the standard multiplicative generators recommended by Control Data Corporation [3] and IBM [2]. The results indicate that the multiplicative generators require much less computer time than the Burford additive generator. Further, the statistical validity of the Burford generator depends upon the initial numbers chosen for use.     

无统计信息假设下的多阶段报童决策

在不对需求做任何统计假设的情形下,该文用理论计算科学兴起的集成专家意见的弱集成算法研究多阶段报童决策。弱集成算法是一种指数加权平均集成方法,在一定的初始权重下,根据损失函数在线调整专家意见的权重。基于收益损失函数和固定订购量的专家意见,得到了与从累积收益角度研究相一致的决策方法;并扩展研究了带有回收价值的情形。理论上证明了决策方法的累积收益损失几乎不超过最优专家意见的累积收益损失。通过数值算例验证了决策方法的可行性和合理性,探讨了卖出价和成本价等因素对竞争性能的影响,说明了回收价值的引入大大提高了决策方法的竞争性能,具有重要的现实意义。     

Hybrid and Size‐Corrected Subsampling Methods

This paper considers inference in a broad class of nonregular models. The models considered are nonregular in the sense that standard test statistics have asymptotic distributions that are discontinuous in some parameters. It is shown in Andrews and Guggenberger (2009a) that standard fixed critical value, subsampling, and m out of n bootstrap methods often have incorrect asymptotic size in such models. This paper introduces general methods of constructing tests and confidence intervals that have correct asymptotic size. In particular, we consider a hybrid subsampling/fixed‐critical‐value method and size‐correction methods. The paper discusses two examples in detail. They are (i) confidence intervals in an autoregressive model with a root that may be close to unity and conditional heteroskedasticity of unknown form and (ii) tests and confidence intervals based on a post‐conservative model selection estimator.     

SORTING AND THE OUTPUT LOSS DUE TO SEARCH FRICTIONS

We analyze a general search model with on‐the‐job search (OJS) and sorting of heterogeneous workers into heterogeneous jobs. For given values of nonmarket time, the relative efficiency of OJS, and the amount of search frictions, we derive a simple relationship between the unemployment rate, mismatch, and wage dispersion. We estimate the latter two from standard micro data. Our methodology accounts for measurement error, which is crucial to distinguish true from spurious mismatch and wage dispersion. We find that without frictions, output would be about 9.5% higher if firms can commit to pay wages as a function of match quality and 15.5% higher if they cannot. Noncommitment leads to a business‐stealing externality which causes a 5.5% drop in output.     

A conceptual model for measuring supply chain's performance

In this article, a conceptual model for measuring supply chain (SC) performance is proposed which can be used for most organisations with the same class at various industries. The model has been developed according to performance metrics interdependencies and some existing shortcomings in the available literature of performance models. Furthermore, it has tried to see the key features of a performance evaluation model. The methodology which is used for solving and integrating the model is a combination of the analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) methods. The DEMATEL and AHP are used for understanding the relationship between comparison metrics and integration to provide a value for performance, respectively.     

考虑属性关联的两阶段获胜者确定模型

研究了多属性逆向拍卖的获胜者确定问题。考虑属性之间的两两关联，以定义在2-可加模糊测度上的Choquet积分表达拍卖人的偏好，其中模糊测度的值由拍卖人提供的偏好信息推测得出。由于一般情况下与偏好信息一致的模糊测度取值并不唯一，考虑所有一致的评分函数提出了两阶段获胜者确定方法。首先采用线性规划挑选出在任意评分函数下可能获胜的报价，再通过混合整数规划确定一个与所有一致的评分函数的评价结果最为接近的报价排序，以得分最高者为稳健获胜报价。仿真实验表明，大量的报价为不可能获胜报价，说明了在第一阶段进行筛选的必要性。与现有方法的比较表明了该方法的有效性，且在拍卖轮数较大、报价数目较多时，该方法在计算效率上更有优势。