A three-stage hybrid approach for weight assignment in MADM

How to determine weights for attributes is one of the key issues in multiple attribute decision making (MADM). This paper aims to investigate a new approach for determining attribute weights based on a data envelopment analysis (DEA) model without explicit inputs (DEA-WEI) and minimax reference point optimisation. This new approach first considers a set of preliminary weights and the most favourite set of weights for each alternative or decision making unit (DMU) and then aggregates these weight sets to find the best compromise weights for attributes with the interests of all DMUs taken into account fairly and simultaneously. This approach is intended to support the solution of such MADM problems as performance assessment and policy analysis where (a) the preferences of decision makers (DMs) are either unclear and partial or difficult to acquire and (b) there is a need to consider the best "will" of each DMU. Two case studies are conducted to show the property of this new proposed approach and how to use it to determine weights for attributes in practice. The first case is about the assessment of research strengths of EU-28 member countries under certain measures and the second is for analysing the performances of Chinese Project 985 universities, where the weights of the attributes need to be assigned in a fair and unbiased manner.     

DEA for non-homogenous parallel networks

In practice, systems are often composed of a group of sub-units. Each sub-unit has a set of performance metrics that are classified as inputs and outputs in data envelopment analysis (DEA). Conventional DEA views such a system as a “black-box”, other DEA-based models are developed to investigate the inner structure, either with a serial structure where components are connected by intermediate products, or with a parallel system under the key assumption that all sub-units are associated with the same type of inputs and outputs (in differing amounts) without the links. In many applications, however, this property of identical input/output factors may not hold. For example, factories may have various manufacturing lines whose inputs and outputs differ from one another. The current paper proposes a series of DEA models to accommodate settings where non-homogenous sub-units operate in parallel network structures with intermediate measures or links. Both the overall performance of the entire parallel network system and efficiency decomposition for each sub-unit can be evaluated through our method.     

The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects

Determining the least distance to the efficient frontier for estimating technical inefficiency, with the consequent determination of closest targets, has been one of the relevant issues in recent Data Envelopment Analysis literature. This new paradigm contrasts with traditional approaches, which yield furthest targets. In this respect, some techniques have been proposed in order to implement the new paradigm. A group of these techniques is based on identifying all the efficient faces of the polyhedral production possibility set and, therefore, is associated with the resolution of a NP-hard problem. In contrast, a second group proposes different models and particular algorithms to solve the problem avoiding the explicit identification of all these faces. These techniques have been applied more or less successfully. Nonetheless, the new paradigm is still unsatisfactory and incomplete to a certain extent. One of these challenges is that related to measuring technical inefficiency in the context of oriented models, i.e., models that aim at changing inputs or outputs but not both. In this paper, we show that existing specific techniques for determining the least distance without identifying explicitly the frontier structure for graph measures, which change inputs and outputs at the same time, do not work for oriented models. Consequently, a new methodology for satisfactorily implementing these situations is proposed. Finally, the new approach is empirically checked by using a recent PISA database consisting of 902 schools.     

Measuring performance of two-stage network structures by DEA: A review and future perspective

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to the second stage. The current paper reviews these models and establishes relations among various approaches. We show that all the existing approaches can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. Future perspectives and challenges are discussed.     

Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.     

存在保证域的模糊非径向偏好DEA模型——基于中科院24个研究所的实证分析

数据包络分析（DEA）是一种非参数化的方法,用于评价具有类似输入和输出的决策单元的效率。传统的非径向DEA模型假设输入和输出数据均为准确值,且对权重变量不加以限制,本文构建了存在保证域的模糊非径向偏好DEA模型,并给出了一种基于模糊数截集的模型求解方法,有效地解决了输入和输出全部或部分为模糊数的决策单元评价问题。最后给出了一个中科院研究所效率评价的实例说明了方法的有效性。     

Risk-Management and Risk-Analysis-Based Decision Tools for Attacks on Electric Power

Incident data about disruptions to the electric power grid provide useful information that can be used as inputs into risk management policies in the energy sector for disruptions from a variety of origins, including terrorist attacks. This article uses data from the Disturbance Analysis Working Group (DAWG) database, which is maintained by the North American Electric Reliability Council (NERC), to look at incidents over time in the United States and Canada for the period 1990-2004. Negative binomial regression, logistic regression, and weighted least squares regression are used to gain a better understanding of how these disturbances varied over time and by season during this period, and to analyze how characteristics such as number of customers lost and outage duration are related to different characteristics of the outages. The results of the models can be used as inputs to construct various scenarios to estimate potential outcomes of electric power outages, encompassing the risks, consequences, and costs of such outages.     

Identification and Review of Sensitivity Analysis Methods

Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.     

动态市场竞争环境下电子商务企业盈利能力的影响因素研究

为找到电子商务企业高投入、低投资回报现象存在的原因,本文首先运用数据包络分析（DEA）模型计算了电子商务企业投入对产出的过度效应,即投入拥挤,然后将之与行业内的市场竞争程度和移动电子商务的发展相结合,通过构建面板数据回归模型,详细探讨了动态市场竞争环境下电子商务企业盈利能力的影响因素。研究结果证实了资产规模过大对传统电子商务企业盈利能力具有削弱作用,而市场集中度和移动电子商务的发展则对传统电子商务企业盈利能力具有推动作用。这些发现启示企业经营管理者应聚焦于移动电子商务发展带来的新机遇,消除投入拥挤,通过调适生产规模和新业务模式的创新,开辟新的营销渠道和盈利模式。     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

多层次结构DEA模型及其应用

在评价实践中,评价者通常倾向采用一组指标或指标体系对DMUs展开相对全面的评价,不同层次指标的重要性也往往不同。然而,标准DEA模型在DMUs有限条件下经常会面临无法直接处理过多的评价指标,也无法直接处理多层次指标的问题。本文从DEA模型中的序结构和测度出发,构建了符合多层次指标体系特点,能相对全面地体现评价者价值导向,且具有合理区分能力的多层次结构DEA模型,并以15个机构基础研究的投入-产出评价为例展开了应用分析。     

Educational objectives and measurement of outcomes

Correlation and regression analysis are often used to infer causal relationships in dynamic systems, even though computed on cross-sectional static data. In education these analytic techniques have been used to support assertions that school-controlled variables make little contribution to student learning. Critics of these assertions point to the low quality of the data, but it may be that the techniques themselves are inappropriate for the development of inferences of causality. This study simulated four possible models of dynamic relationships between family and school inputs and achievement outcomes. The models were run for five periods. Data generated were submitted to correlation and regression analysis. Both unique variance and regression coefficient indicators failed to describe reliably causal relationships built into the models. Conclusion: complex systems resist simplistic analyses.     

Use of Sensitivity Analysis to Assess Reliability of Metabolic and Physiological Models

Because ethical considerations often preclude directly determining the human health effects of treatments or interventions by experimentation, such effects are estimated by extrapolating reactions predicted from animal experiments. Under such conditions, it must be demonstrated that the reliability of the extrapolated predictions is not excessively affected by inherent data limitations and other components of model specification. This is especially true of high-level models composed of ad hoc algebraic equations whose parameters do not correspond to specific physical properties or processes. Models based on independent experimental data restricting the numerical space of parameters that do represent actual physical properties can be represented at a more detailed level. Sensitivities of the computed trajectories to parameter variations permit more detailed attribution of uncertainties in the predictions to these low-level properties. S-systems, in which parameters are estimated empirically, and physiological models, whose parameters can be estimated accurately from independent data, are used to illustrate the applicability of trajectory sensitivity analysis to lower-level models.     

An Efficient Algorithm for Delay Buffer Minimization

A data flow machine is said to be synchronized if for any vertex u in the underlying data flow graph, all inputs to vertex u arrive at the same time. An unsynchronized data flow machine with an acyclic underlying data flow graph can be transformed into a synchronized system by adding unit delay buffers to the system. This synchronization process can increase pipelining and throughout. Since the addition of delay buffers introduces hardware and area costs, it is desirable to insert the minimum number of delay buffers to synchronize a given data flow machine. Due to important applications in computer design, various delay buffer minimization problems have been studied by many researchers. Several optimal algorithms and heuristic algorithms have been proposed for slightly different models. In this paper, we introduce the concept of extensions of a directed acyclic graph to generalize and formalize several delay buffer minimization problems studied in the literature and present a polynomial time algorithm for computing the minimum delay buffer synchronization of a given data flow machine. Examples are provided to illustrate our algorithm and to show that our algorithm requires fewer delay buffers than previously published optimal algorithms for various models.     

环保项目有效性评价的数据包络分析方法

利用数据包络分析(DEA)方法评价环保项目有效性时,输入、输出数据中可能同时存在环境因素,不符合传统的DEA模型要求,需要对这些数据进行转换。本文讨论了"不好的"数据平移转换法、输入和输出因素转换法、倒数转换法,建立了三种DEA模型,对建立的DEA模型一致性进行了分析,拓宽了以前DEA模型的应用范围。一个例子验证了这三种方法在评价环保项目有效性时是一致的。     

Evaluating input changes in a given facility—Iron making

This paper examines the effects of changes in the nature and mix of inputs on an otherwise essentially stable processing technology. The impact of the benefication and agglomeration (pelletizing) of iron ore on blast furnace productivity is shown to transcend both the usually postulated, overly simplified, models of such effects and also the expectations of managements themselves. Analysis of the physical performance and financial data for three major American blast furnace installations demonstrates the pervasive changes brought about by what, at first glance, may seem no more than a simple substitution among inputs.     

存在非期望输入输出的多阶段系统效率评价模型

现有多阶段DEA模型的研究普遍假设所有输入、输出均为期望指标, 对存在非期望指标的情况研究较少, 尚无对最初投入和中间产出带有非期望指标的研究。为此, 本文首先提出了输入、输出类型的判定方法, 并将其应用于存在非期望输入、输出的两阶段系统。进一步针对存在非期望指标的两阶段生产系统中同种输入、输出, 尤其中间产出类型判定一致与不一致的情况, 构建了相应的生产可能集以及两阶段DEA模型, 最后利用本文方法和模型对我国上市银行效率进行了评价。     

Expert Review on Poliovirus Immunity and Transmission

Successfully managing risks to achieve wild polioviruses (WPVs) eradication and address the complexities of oral poliovirus vaccine (OPV) cessation to stop all cases of paralytic poliomyelitis depends strongly on our collective understanding of poliovirus immunity and transmission. With increased shifting from OPV to inactivated poliovirus vaccine (IPV), numerous risk management choices motivate the need to understand the tradeoffs and uncertainties and to develop models to help inform decisions. The U.S. Centers for Disease Control and Prevention hosted a meeting of international experts in April 2010 to review the available literature relevant to poliovirus immunity and transmission. This expert review evaluates 66 OPV challenge studies and other evidence to support the development of quantitative models of poliovirus transmission and potential outbreaks. This review focuses on characterization of immunity as a function of exposure history in terms of susceptibility to excretion, duration of excretion, and concentration of excreted virus. We also discuss the evidence of waning of host immunity to poliovirus transmission, the relationship between the concentration of poliovirus excreted and infectiousness, the importance of different transmission routes, and the differences in transmissibility between OPV and WPV. We discuss the limitations of the available evidence for use in polio risk models, and conclude that despite the relatively large number of studies on immunity, very limited data exist to directly support quantification of model inputs related to transmission. Given the limitations in the evidence, we identify the need for expert input to derive quantitative model inputs from the existing data.