Efficiency measurement in fuzzy additive data envelopment analysis

Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms characterised by fuzzy numbers in DEA to reflect the decision-makers' intuition and subjective judgements. This paper extends the conventional DEA models to a fuzzy framework by proposing a new fuzzy additive DEA model for evaluating the efficiency of a set of decision-making units (DMUs) with fuzzy inputs and outputs. The contribution of this paper is threefold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA, (2) we propose a new fuzzy additive DEA model derived from the a-level approach and (3) we demonstrate the practical aspects of our model with two numerical examples and show its comparability with five different fuzzy DEA methods in the literature. Copyright © 2011 Inderscience Enterprises Ltd.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Dynamic measurement of accommodation and pupil size using the portable Grand Seiko FR-5000 autorefractor

PURPOSE. The purpose of this study was to evaluate the potential of the portable Grand Seiko FR-5000 autorefractor to allow objective, continuous, open-field measurement of accommodation and pupil size for the investigation of the visual response to real-world environments and changes in the optical components of the eye. METHODS. The FR-5000 projects a pair of infrared horizontal and vertical lines on either side of fixation, analyzing the separation of the bars in the reflected image. The measurement bars were turned on permanently and the video output of the FR-5000 fed into a PC for real-time analysis. The calibration between infrared bar separation and the refractive error was assessed over a range of 10.0 D with a model eye. Tolerance to longitudinal instrument head shift was investigated over a ±15 mm range and to eye alignment away from the visual axis over eccentricities up to 25.0°. The minimum pupil size for measurement was determined with a model eye. RESULTS. The separation of the measurement bars changed linearly (r = 0.99), allowing continuous online analysis of the refractive state at 60 Hz temporal and approximately 0.01 D system resolution with pupils >2 mm. The pupil edge could be analyzed on the diagonal axes at the same rate with a system resolution of approximately 0.05 mm. The measurement of accommodation and pupil size were affected by eccentricity of viewing and instrument focusing inaccuracies. CONCLUSIONS. The small size of the instrument together with its resolution and temporal properties and ability to measure through a 2 mm pupil make it useful for the measurement of dynamic accommodation and pupil responses in confined environments, although good eye alignment is important. Copyright © 2006 American Academy of Optometry.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Nondestructive measurement of water content and moisture migration of unsaturated red clays in South China

To reveal the moisture migration mechanism of the unsaturated red clays, which are sensitive to water content change and widely distributed in South China, and then rationally use them as a filling material for highway embankments, a method to measure the water content of red clay cylinders using X-ray computed tomography (CT) was proposed and verified. Then, studies on the moisture migrations in the red clays under the rainfall and ground water level were performed at different degrees of compaction. The results show that the relationship between dry density, water content, and CT value determined from X-ray CT tests can be used to nondestructively measure the water content of red clay cylinders at different migration time, which avoids the error reduced by the sample-to-sample variation. The rainfall, ground water level, and degree of compaction are factors that can significantly affect the moisture migration distance and migration rate. Some techniques, such as lowering groundwater table and increasing degree of compaction of the red clays, can be used to prevent or delay the moisture migration in highway embankments filled with red clays.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

Clogging in subsurface-flow treatment wetlands:measurement, modeling and management

This paper reviews the state of the art in measuring, modeling, and managing clogging in subsurface-flow treatment wetlands. Methods for measuring in situ hydraulic conductivity in treatment wetlands are now available, which provide valuable insight into assessing and evaluating the extent of clogging. These results, paired with the information from more traditional approaches (e.g., tracer testing and composition of the clog matter) are being incorporated into the latest treatment wetland models. Recent finite element analysis models can now simulate clogging development in subsurface-flow treatment wetlands with reasonable accuracy. Various management strategies have been developed to extend the life of clogged treatment wetlands, including gravel excavation and/or washing, chemical treatment, and application of earthworms. These strategies are compared and available cost information is reported. © 2012 Elsevier Ltd.

New Knowledge Obtaining in Structural-predicate Models of Knowledge

An effective mathematical method of new knowledge obtaining on the structure of complex objects with required properties is developed. The method comprehensively takes into account information on the properties and relations of primary objects, composing the complex objects. It is based on measurement of distances between the predicate groups with some interpretation of them. The optimal measure for measurement of these distances with the maximal discernibleness of different groups of predicates is constructed. The method is tested on solution of the problem of obtaining of new compound with electro-optical properties.

Minimum Mean Square Error Approach in Images Processing for Full-field Displacements and Deformation Measurements

A vision system is applied to full-field displacements and deformation measurements in solid mechanics. A speckle like pattern is preliminary formed on the surface under investigation. To determine displacements field of one speckle image with respect to a reference speckle image, sub-images, referred to Zones Of Interest (ZOI) are considered. The field is obtained by matching a ZOI in the reference image with the respective ZOI in the moved image. Two image processing techniques are used for implementing the matching procedure: – cross correlation function and minimum mean square error (MMSE) of the ZOI intensity distribution. The two algorithms are compared and the influence of the ZOI size on the accuracy of measurements is studied.

Time resolved bit error rate analysis of a fast switching tunable laser for use in optically switched networks

We investigate the use of different direct detection modulation formats in a wavelength switched optical network. We find the minimum time it takes a tunable sampled grating distributed Bragg reflector laser to recover after switching from one wavelength channel to another for different modulation formats. The recovery time is investigated utilizing a field programmable gate array which operates as a time resolved bit error rate detector. The detector offers 93 ps resolution operating at 10.7 Gb/s and allows for all the data received to contribute to the measurement, allowing low bit error rates to be measured at high speed. The recovery times for 10.7 Gb/s non-return-to-zero on–off keyed modulation, 10.7 Gb/s differentially phase shift keyed signal and 21.4 Gb/s differentially quadrature phase shift keyed formats can be as low as 4 ns, 7 ns and 40 ns, respectively. The time resolved phase noise associated with laser settling is simultaneously measured for 21.4 Gb/s differentially quadrature phase shift keyed data and it shows that the phase noise coupled with frequency error is the primary limitation on transmitting immediately after a laser switching event.

Identification of key temperature measurement technologies for the enhancement of product and equipment integrity in the light controlled factory

Thermal effects in uncontrolled factory environments are often the largest source of uncertainty in large volume dimensional metrology. As the standard temperature for metrology of 20°C cannot be achieved practically or economically in many manufacturing facilities, the characterisation and modelling of temperature offers a solution for improving the uncertainty of dimensional measurement and quantifying thermal variability in large assemblies. Technologies that currently exist for temperature measurement in the range of 0-50°C have been presented alongside discussion of these temperature measurement technologies' usefulness for monitoring temperatures in a manufacturing context. Particular aspects of production where the technology could play a role are highlighted as well as practical considerations for deployment. Contact sensors such as platinum resistance thermometers can produce accuracy closest to the desired accuracy given the most challenging measurement conditions calculated to be ∼0.02°C. Non-contact solutions would be most practical in the light controlled factory (LCF) and semi-invasive appear least useful but all technologies can play some role during the initial development of thermal variability models.

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors.

Real-time error compensation of a three-axis machine tool using a laser tracker

The concept of measurement-enabled production is based on integrating metrology systems into production processes and generated significant interest in industry, due to its potential to increase process capability and accuracy, which in turn reduces production times and eliminates defective parts. One of the most promising methods of integrating metrology into production is the usage of external metrology systems to compensate machine tool errors in real time. The development and experimental performance evaluation of a low-cost, prototype three-axis machine tool that is laser tracker assisted are described in this paper. Real-time corrections of the machine tool's absolute volumetric error have been achieved. As a result, significant increases in static repeatability and accuracy have been demonstrated, allowing the low-cost three-axis machine tool to reliably reach static positioning accuracies below 35 μm throughout its working volume without any prior calibration or error mapping. This is a significant technical development that demonstrated the feasibility of the proposed methods and can have wide-scale industrial applications by enabling low-cost and structural integrity machine tools that could be deployed flexibly as end-effectors of robotic automation, to achieve positional accuracies that were the preserve of large, high-precision machine tools.

Measurement of scleral thickness in humans using anterior segment optical coherent tomography

Anterior segment optical coherent tomography (AS-OCT, Visante; Zeiss) is used to examine meridional variation in anterior scleral thickness (AST) and its association with refractive error, ethnicity and gender. Scleral cross-sections of 74 individuals (28 males; 46 females; aged between 18-40 years (27.7±5.3)) were sampled twice in random order in 8 meridians: [superior (S), inferior (I), nasal (N), temporal (T), superior-temporal (ST), superior-nasal (SN), inferior-temporal (IT) and inferior-nasal (IN)]. AST was measured in 1mm anterior-toposterior increments (designated the A-P distance) from the scleral spur (SS) over a 6mm distance. Axial length and refractive error were measured with a Zeiss IOLMaster biometer and an open-view binocular Shin-Nippon autorefractor. Intra- And inter-observer variability of AST was assessed for each of the 8 meridians. Mixed repeated measures ANOVAs tested meridional and A-P distance differences in AST with refractive error, gender and ethnicity. Only right eye data were analysed. AST (mean±SD) across all meridians and A-P distances was 725±46μm. Meridian SN was the thinnest (662±57μm) and I the thickest (806 ±60μm). Significant differences were found between all meridians (p<0.001), except S:ST, IT:IN, IT:N and IN:N. Significant differences between A-P distances were found except between SS and 6 mm and between 2 and 4mm. AST measurements at 1mm (682±48 μm) were the thinnest and at 6mm (818±49 μm) the thickest (p<0.001); a significant interaction occurred between meridians and A-P distances (p<0.001). AST was significantly greater (p<0.001) in male subjects but no significant differences were found between refractive error or ethnicity. Significant variations in AST occur with regard to meridian and distance from the SS and may have utility in selecting optimum sites for pharmaceutical or surgical intervention.