Fuzzy assessment of performance of a decision making units using DEA:a non-radical approach

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the a-level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include a-level to the model under fuzzy environment. An example is given to illustrate this method in details.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

Estimating the scope for savings in referrals and drug prescription costs in the general Practice units of a UK primary care trust

This paper explores the potential for cost savings in the general Practice units of a Primary Care Trust (PCT) in the UK. We have used Data Envelopment Analysis (DEA) to identify benchmark Practices, which offer the lowest aggregate referral and drugs costs controlling for the number, age, gender, and deprivation level of the patients registered with each Practice. For the remaining, non-benchmark Practices, estimates of the potential for savings on referral and drug costs were obtained. Such savings could be delivered through a combination of the following actions: (i) reducing the levels of referrals and prescriptions without affecting their mix (£15.74 m savings were identified, representing 6.4% of total expenditure); (ii) switching between inpatient and outpatient referrals and/or drug treatment to exploit differences in their unit costs (£10.61 m savings were identified, representing 4.3% of total expenditure); (iii) seeking a different profile of referral and drug unit costs (£11.81 m savings were identified, representing 4.8% of total expenditure). © 2012 Elsevier B.V. All rights reserved.

Using DEA to estimate potential savings at GP units at medical specialty level

The aim of this paper is to identify benchmark cost-efficient General Practitioner (GP) units at delivering health care in the Geriatric and General Medicine (GMG) specialty and estimate potential cost savings. The use of a single medical specialty makes it possible to reflect more accurately the medical condition of the List population of the Practice so as to contextualize its expenditure on care for patients. We use Data Envelopment Analysis (DEA) to estimate the potential for cost savings at GP units and to decompose these savings into those attributable to the reduction of resource use, to altering the mix of resources used and to those attributable to securing better resource 'prices'. The results reveal a considerable potential for savings of varying composition across GP units. © 2013 Elsevier Ltd.

The Information-analytical System for Diagnostics of Aircraft Navigation Units

The operation of technical processes requires increasingly advanced supervision and fault diagnostics to improve reliability and safety. This paper gives an introduction to the field of fault detection and diagnostics and has short methods classification. Growth of complexity and functional importance of inertial navigation systems leads to high losses at the equipment refusals. The paper is devoted to the INS diagnostics system development, allowing identifying the cause of malfunction. The practical realization of this system concerns a software package, performing a set of multidimensional information analysis. The project consists of three parts: subsystem for analyzing, subsystem for data collection and universal interface for open architecture realization. For a diagnostics improving in small analyzing samples new approaches based on pattern recognition algorithms voting and taking into account correlations between target and input parameters will be applied. The system now is at the development stage.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

A spatiotemporal Data Envelopment Analysis (S-T DEA) approach:the need to assess evolving units

One of the major challenges in measuring efficiency in terms of resources and outcomes is the assessment of the evolution of units over time. Although Data Envelopment Analysis (DEA) has been applied for time series datasets, DEA models, by construction, form the reference set for inefficient units (lambda values) based on their distance from the efficient frontier, that is, in a spatial manner. However, when dealing with temporal datasets, the proximity in time between units should also be taken into account, since it reflects the structural resemblance among time periods of a unit that evolves. In this paper, we propose a two-stage spatiotemporal DEA approach, which captures both the spatial and temporal dimension through a multi-objective programming model. In the first stage, DEA is solved iteratively extracting for each unit only previous DMUs as peers in its reference set. In the second stage, the lambda values derived from the first stage are fed to a Multiobjective Mixed Integer Linear Programming model, which filters peers in the reference set based on weights assigned to the spatial and temporal dimension. The approach is demonstrated on a real-world example drawn from software development.

Units of F5kD10

2000 Mathematics Subject Classification: 20C05, 16U60, 16S84, 15A33.

Automated Generating of Test Units for Evaluation of Cognitive and Language Skills

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Heterogenitás és technikai hatékonyság - a magyar specializált szántóföldi növénytermesztő üzemek esete = Heterogeneity and technical efficiency - the case of Hungary's specialized arable crop production units

A cikkben paneladatok segítségével a magyar gabonatermesztő üzemek 2001 és 2009 közötti technikai hatékonyságát vizsgáljuk. A technikai hatékonyság szintjének becslésére egy hagyományos sztochasztikus határok modell (SFA) mellett a látens csoportok modelljét (LCM) használjuk, amely figyelembe veszi a technológiai különbségeket is. Eredményeink arra utalnak, hogy a technológiai heterogenitás fontos lehet egy olyan ágazatban is, mint a szántóföldi növénytermesztés, ahol viszonylag homogén technológiát alkalmaznak. A hagyományos, azonos technológiát feltételező és a látens osztályok modelljeinek összehasonlítása azt mutatja, hogy a gabonatermesztő üzemek technikai hatékonyságát a hagyományos modellek alábecsülhetik. _____ The article sets out to analyse the technical efficiency of Hungarian crop farms between 2001 and 2009, using panel data and employing both standard stochastic frontier analysis and the latent class model (LCM) to estimate technical efficiency. The findings suggest that technological heterogeneity plays an important role in the crop sector, though it is traditionally assumed to employ homogenous technology. A comparison of standard SFA models that assumes the technology is common to all farms and LCM estimates highlights the way the efficiency of crop farms can be underestimated using traditional SFA models.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.

Factors affecting emergency department length of stay of patients admitted to telemetry and critical care units

The purpose of this study was to determine the emergency department (ED) length of stay (LOS) of patients admitted to inpatient telemetry and critical care units and to identify the factors that contribute to a prolonged ED LOS. It also examined whether there was a difference in ED LOS between clients evaluated by an ED physician, an Advanced Registered Nurse Practitioner (ARNP) or a Physician's Assistant (PA).^ A data collection tool was devised and used to record data obtained by retrospectively reviewing 110 charts of patients from this sample. The mean ED LOS was 286.75 minutes. Multiple factors were recorded as affecting the ED LOS of this sample, including: age, diagnosis, consultations, multiple radiographs, pending admission orders, nurse unable to call report/busy, relatives at bedside, observation or stabilization necessary, bed not ready and infusion in progress. No significant difference in ED LOS was noted between subjects initially evaluated by a physician, an ARNP or a PA. ^