Dual-role factors in data envelopment analysis

This paper presents a methodology for dealing with performance evaluation settings where factors can simultaneously play both input and output roles. Model structures are developed for classifying Decision-Making Units (DMUs) into three groups according to whether such a factor is behaving like an output, an input, or is in equilibrium, neither wanting to lose or gain any of the factors. We connect these ideas to those involving increasing, decreasing and constant returns to scale. Examples of factors that play this dual-role are: trainees in organizations, such as nurses, medical students, and doctoral students; awards to scholars or university departments; certain revenue—generating transactions in banks, and so on. We apply the model to the analysis of a set of university departments. In some settings, a dual-role factor may be one that can be reallocated, such as would be the case when DMUs are managed by a central authority. We develop the appropriate model structures to permit such a reallocation. We present two such structures, with the first involving reallocation from an existing allocation, and the second, a zero-base allocation.     

Output deterioration with input reduction in data envelopment analysis

In most practical applications of Data Envelopment Analysis (DEA), it is recognized that there is a gap between the theoretical maximum reduction in inputs, e.g., 1 -θ, versus what is actually achievable. In this paper we examine this phenomenon in the context of productivity measurement of highway maintenance crews. Here, maintenance supervisors and geotechnical engineers estimate the maximum achievable reduction in resources without impacting the outputs from the process. Furthermore, they put bounds on the extent of output erosion that can result from input reduction beyond this maximum level. We present a modified version of the standard Banker, Charnes and Cooper (BCC) DEA model that explicitly addresses this erosion phenomenon. The resulting projections provide maintenance managers with a measure of the impact on system performance under excessive resource reduction, and aid in setting guidelines for maintenance budgeting at the patrol level.     

Inter-country R&;D efficiency analysis: An application of data envelopment analysis

This study examines the relative efficiency of the R&D process across a group of 22 developed and developing countries using Data Envelopment Analysis (DEA). The R&D technical efficiency is examined using a model with patents granted to residents as an output and gross domestic expenditure on R&D and the number of researchers as inputs. Under CRS (Constant Returns to Scale), Japan, the Republic of Korea and China are found to be efficient, whereas under the VRS (Variable Returns to Scale) framework, Japan, the Republic of Korea, China, India, Slovenia and Hungary are found to be efficient. The emergence of some of the developing nations on the efficiency frontier indicates that these nations can also serve as benchmarks for their efficient use of R&D resources. The inefficiency in the R&D resource usage highlighted by this study indicates the underlying potential that can be tapped for the development and growth of nations.     

An imprecise Dirichlet model for Bayesian analysis of failure data including right-censored observations

This paper is intended to make researchers in reliability theory aware of a recently introduced Bayesian model with imprecise prior distributions for statistical inference on failure data, that can also be considered as a robust Bayesian model. The model consists of a multinomial distribution with Dirichlet priors, making the approach basically nonparametric. New results for the model are presented, related to right-censored observations, where estimation based on this model is closely related to the product-limit estimator, which is an important statistical method to deal with reliability or survival data including right-censored observations. As for the product-limit estimator, the model considered in this paper aims at not using any information other than that provided by observed data, but our model fits into the robust Bayesian context which has the advantage that all inferences can be based on probabilities or expectations, or bounds for probabilities or expectations. The model uses a finite partition of the time-axis, and as such it is also related to life-tables.     

Assessing sustainability of Islamic countries via data envelopment analysis (DEA)

Clean Technologies and Environmental Policy - Given international regulations, countries try to improve their sustainability. The objective of this paper is to assess the sustainability of Islamic...     

Application of data envelopment analysis for cell performance evaluation and process improvement in cellular manufacturing

This paper proposes a methodology for cell performance evaluation and improvement which considers multiple cell inputs and outputs. A specific technique in data envelopment analysis called 'window analysis', which captures the cell efficiency changes over time, is modified and utilized in the methodology. The evaluation is performed by considering the local part families (manufactured in single cells) and the infrequent parts (manufactured in multiple cells) processed by a cell. A major contribution of this research is in proposing a new 'modified window analysis' technique for cell performance evaluation, and in demonstrating its effectiveness over the 'traditional window analysis'. Another contribution is in using the cross efficiency matrix to identify periods of best cell operating practices which aid management in cell process improvement.     

Robust design with imprecise random variables and its application in hydrokinetic turbine optimization

In robust design, uncertainty is commonly modelled with precise probability distributions. In reality, the distribution types and distribution parameters may not always be available owing to limited data. This research develops a robust design methodology to accommodate the mixture of both precise and imprecise random variables. By incorporating the Taguchi quality loss function and the minimax regret criterion, the methodology mitigates the effects of not only uncertain parameters but also uncertainties in the models of the uncertain parameters. Hydrokinetic turbine systems are a relatively new alternative energy technology, and both precise and imprecise random variables exist in the design of such systems. The developed methodology is applied to the robust design optimization of a hydrokinetic turbine system. The results demonstrate the effectiveness of the proposed methodology.     

Value-at-risk and data envelopment analysis: comments on Wu and Olson (2010)

In this note, we identify three major errors contained in Wu and Olson [Wu, D.D. and Olson, D., 2010. Enterprise risk management: a DEA VaR approach in vendor selection. International Journal of Production Research, 48 (16), 4919–4932]. It is shown that the ‘DEA VaR’ model named by the authors is not truly a Value-at-risk (VaR) minimisation problem. It is also pointed out that the authors confused two concepts of stochastic efficiency. Finally, it is revealed that the linearisation technique proposed by the authors is questionable under some conditions and a correction is suggested.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

随机荷载下陡波形立管的非线性动力分析EI北大核心CSCD

对陡波形立管在随机荷载作用下的非线性动力响应进行了数值分析。考虑内部流体,基于柔性杆理论建立了陡波形立管(SWR)的数值模型,采用Newmark-β法求解立管动力响应,利用MATLAB软件编写相应计算程序DRSWR,将DRSWR的计算结果与OrcaFlex进行对比验证,然后对陡波形立管在随机波浪、顶部浮体随机运动共同激励下的应力响应进行了参数敏感性分析。结果表明:浮子段应力水平、应力变化幅值最高;应力极值点为拱弯点和垂弯点;大波高、低周期的波浪会导致立管整体应力水平急剧升高;平均静偏移增大导致立管应力极值减小;大幅高频的慢漂运动会使悬挂点应力显著增大;高密度、高流速内部流体会使陡波形立管处于高应力状态。     

Response analysis of stochastic parameter structures under non-stationary random excitation

 The stochastic orthogonal polynomial expansion method is extended with the pseudo-excitation method in this paper. This extension enables the stochastic orthogonal polynomial method to be readily used in the analysis of stochastic parameter structures under non-stationary random excitation. The probabilistic information of structural response, such as the power spectral density, standard deviation function, etc. can be obtained directly with this method. A dynamic condensation algorithm for order-expanded equation resulting from the orthogonal polynomial expansion method is also presented in this paper. The applicability of the proposed methodology is demonstrated by numerical examples. Received 21 July 2000     

Combining reference point based composite indicators with data envelopment analysis: application to the assessment of universities

In our knowledge society, where universities are key players, the assessment of higher education institutions should meet the new demands of the present complex environment. This calls for the use of techniques that are able to manage this complexity. In this paper, we propose a novel combination of methodologies, jointly using a multi-criteria reference point scheme and the data envelopment analysis (DEA) for the assessment of universities. This combination allows us to take into account all the aspects regarded as relevant to assess university performance, and use them as outputs in the efficiency analysis. Our findings highlight the convenience to assess the university performance by using both compensatory and non-compensatory schemes. This way, the information provided allows to detect the actions needed to improve the performances of the universities, rather than just giving an overall performance measure. Furthermore, combining the use of composite indicators with the DEA analysis provides a more complete picture of the institutions assessed, allowing universities to check their efficiency and to detect their weaknesses and strengths accordingly. The approach is illustrated using data of 47 Spanish public universities for the academic year, 2016–2017.

     

New robust bilinear least squares method for the analysis of spectral-pH matrix data

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.     

The motivations for knowledge transfer across borders: the diffusion of data envelopment analysis (DEA) methodology

To facilitate technology development, people rely on quick and intensive knowledge interactions without barriers. However, when people need to transfer knowledge from one place to another, geographical distance is a critical barrier to overcome because tacit and invisible characteristics are embedded in certain knowledge and locations. This study explores how social and scientific resources embedded within persons can motivate personal knowledge-diffusion behaviors; that is, bridging resources between locations. To explain cross-border diffusion, this work analyzes knowledge dissemination of the data envelopment analysis (DEA) method. By collecting theoretical and application papers in DEA methodology from the Web of Science data set, this study analyzes the academic network consisting of 610 researchers and identifies author locations, research disciplines, and their mutual linkages to explain the importance of personal specific characteristics in cross-border diffusion. Regression models and network analysis show the advantages of personal research seniority and cross-disciplinary coordinating capabilities for researchers to diffuse knowledge from one region to another. The corresponding brokering capabilities accumulated within domestic area or adjacent nations are also helpful for specifically brokering resources of other farther places.     

Calibration of the thermoelastic stress analysis technique under sinusoidal and random loading conditions

For the past five years the National Engineering Laboratory (NEL) has been engaged in a research programme investigating the applications of thermoelastic stress analysis to engineering structures. This paper discusses the use of a uniformly stressed component for the calibration of SPATE (Stress Pattern Analysis by Thermal Emission). Experimental results for sinusoidal loading on a carbon steel are compared with a theoretical prediction based upon material properties and instrument characteristics. A calibration technique which is suitable for the measurement of quantitative stresses under random loading, including modal behaviour, is also described.     

非负矩阵分解在地震作用下结构随机响应分析中的应用

地震动作为一类典型的非平稳随机过程可由演变谱刻画其能量的时-频分布。然而,演变谱的时-频耦合特性却限制了经典谱表示法的模拟效率。为提高非平稳地震动模拟效率,简化非平稳地震作用下结构随机响应分析,提出了基于非负矩阵分解(nonnegative matrix factorization,NMF)的地震动演变谱解耦方案,使结构在非平稳地震作用下的响应计算简化为各项均匀调制激励下的结构随机响应叠加。分析结果表明,基于非负矩阵分解的地震动演变谱解耦具有良好的精度,快速傅里叶变换技术的引入提高了经典谱表示法的模拟效率,模拟样本自相关函数与目标值吻合良好,非平稳地震作用下结构随机响应频域分析得到简化。     

Efficiency assessment of green technology innovation of renewable energy enterprises in China: a dynamic data envelopment analysis considering undesirable output

Clean Technologies and Environmental Policy - The rapid development of renewable energy enterprises has produced important benefits for contemporary efforts to address serious environmental...     

Gasdynamic perturbations under conditions of surface microwave discharge in air

An investigation is performed of gasdynamic perturbations arising in the neighborhood of a dielectric antenna, on the external surface of which a microwave discharge is initiated at high pressures of air, where the electron-molecule collision frequency is much higher than the circular frequency of electromagnetic field. The time dependence of gas temperature is obtained for different values of power input, and it is demonstrated that, in the stage of formation of discharge at atmospheric pressure of air, the gas is heated at a rate of 30–70 K/μs. The electron concentration in plasma channels at atmospheric pressure does not exceed 10¹⁵–10¹⁶ cm⁻³. A study is made of the evolution of shock waves arising under conditions of surface micro-wave discharge at different pressures of surrounding gas, different durations of stimulation, and different power inputs to the discharge. It is demonstrated that the shock wave velocity in the vicinity of the antenna reaches a value of 1 km/s.     

An approach to robust optimization of impact problems using random samples and meta-modelling

Conventionally optimized structures may show a tendency to be sensitive to variations, for instance in geometry and loading conditions. To avoid this, research has been carried out in the field of robust optimization where variations are taken into account in the optimization process. The overall objective is to create solutions that are optimal both in the sense of mean performance and minimum variability. This work presents an alternative approach to robust optimization, where the robustness of each design is assessed through multiple sampling of the stochastic variables at each design point. Meta-models for the robust optimization are created for both the mean value and the standard deviation of the response. Furthermore, the method is demonstrated on an analytical example and an example of an aluminium extrusion with quadratic cross-section subjected to axial crushing. It works well for the chosen examples and it is concluded that the method is especially well suited for problems with a large number of random variables, since the computational cost is essentially independent of the number of random variables. In addition, the presented approach makes it possible to take into consideration variations that cannot be described with a variable. This is demonstrated in this work by random geometrical perturbations described with the use of Gaussian random fields.