1,168 research outputs found
Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach
summary:In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct the equivalent deterministic linear programming model. The relationship between the stochastic efficiency of each stage and stochastic centralized efficiency of the whole process, at different confidence levels, is discussed. To illustrate the real applicability of the proposed approach, a real case on 16 commercial banks in China is given
Stochastic cost efficiency evaluation of a supply chain
The main goal of the paper is a consideration of cost efficiency evaluation models related to some supply chain when dealing with imprecise data. Data envelopment analysis (DEA) method is a non-parametric mathematical programming approach to assess the performance. This method is proposed for deterministic data and it can be generalized to inaccurate data, while considering real world applications. Here we consider data as random variables and after reviewing and introducing new models to evaluate cost efficiencies related to the special circumstances of the supply chain using DEA, these models are developed to probabilistic form. Also, deterministic and linear equivalents are proposed using the symmetric error structure of normal distributions. At final, by a numerical example, the proposed models are examined to show relationships of results.Publisher's Versio
Regulatory and Environmental Effects on Public Transit Efficiency. A Mixed DEA-SFA Approach
The aim of this paper is to account for the impact of statistical noise and exogenous regulatory and environmental factors on the efficiency of public transit systems in a DEA-based framework. To this end, we implement a three-stage DEA-SFA mixed approach based on Fried et al. (2002) using a 1993-1999 panel of 42 Italian public transit companies. This allows us to decompose input-specific DEA inefficiency measures into three components: exogenous effects, pure managerial inefficiency, and statistical noise. First, the initial evaluation of producer performance is carried out using conventional variable returns to scale DEA (Banker et al., 1984). Second, a SFA approach (Battese and Coelli, 1992) is used to regress single input slacks on subsidies regulation (cost-plus versus fixed-price contracts) and a set of environmental variables including network speed and user density. Finally, third stage re-runs DEA on inputs purged of both exogenous effects and statistical noise. Results are such that adjusting for the type of regulatory scheme, environmental conditions, and statistical noise increases average efficiency in the industry and reduces dispersion among firms. Furthermore, the implementation of fixed-price subsidies is found to enhance efficiency in the usage of âdriversâ and âmaterials and servicesâ inputs. Such a result sheds some light on the determinants of input-specific efficiency differentials in the industry, improving the existing evidence on mean overall cost efficiency (e.g. Gagnepain e Ivaldi, 2002; Piacenza, 2006). As a policy implication, it is confirmed the relevance of regula tion aimed at replacing cost-plus subsidization mechanisms with high-powered incentive contracts as well as improving operating conditions of public transport networks.Public transit systems, Regulation, Environmental effects, Statistical noise, Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA)
Effects of strategic alliances' on hospital efficiency and capacity utilization in Mexico
Publisher Copyright: © 2019, Emerald Publishing Limited.Purpose:This paper aims to investigate the efficiency implications of belonging to a strategic hospital alliance (SHA) and measuring the effects over capacity utilization of such agreements in a Mexican healthcare context. Design/methodology/approach: Data Envelopment Analysis (DEA) is the nonparametric methodology used, which supports both objectives. Technological gaps ratios are calculated by using DEA-metafrontier approach to compare efficiency between SHA members and a hospital's control group. Also, hospital capacity utilization ratios are used as the maximum rate of output possible from fixed inputs in a frontier setting using directional distance functions. Data were collected from an alliance called Consorcio Mexicano de Hospitales in MĂ©xico, which has 29 general private hospitals and a group of 47 hospitals with same characteristics from a database made by the Instituto Nacional de EstadĂstica y GeografĂa for year 2014. Findings: The results indicate that efficiency is better at hospitals that belong to an alliance; it also shows an improvement of installed capacity management for hospital alliances in MĂ©xico. Originality/value: The results can be useful for both private health organization managers and regulators themselves to adopt management practices that may end up having a favorable impact on cost and prices containment. Additionally, there are no previous studies neither in Mexico nor in Latin America that analyze the impact of strategic hospitality alliances on the efficiency and utilization of the capacity of private hospitals
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication
This paper has two contributions. First, we propose a novel coded matrix
multiplication technique called Generalized PolyDot codes that advances on
existing methods for coded matrix multiplication under storage and
communication constraints. This technique uses "garbage alignment," i.e.,
aligning computations in coded computing that are not a part of the desired
output. Generalized PolyDot codes bridge between Polynomial codes and MatDot
codes, trading off between recovery threshold and communication costs. Second,
we demonstrate that Generalized PolyDot can be used for training large Deep
Neural Networks (DNNs) on unreliable nodes prone to soft-errors. This requires
us to address three additional challenges: (i) prohibitively large overhead of
coding the weight matrices in each layer of the DNN at each iteration; (ii)
nonlinear operations during training, which are incompatible with linear
coding; and (iii) not assuming presence of an error-free master node, requiring
us to architect a fully decentralized implementation without any "single point
of failure." We allow all primary DNN training steps, namely, matrix
multiplication, nonlinear activation, Hadamard product, and update steps as
well as the encoding/decoding to be error-prone. We consider the case of
mini-batch size , as well as , leveraging coded matrix-vector
products, and matrix-matrix products respectively. The problem of DNN training
under soft-errors also motivates an interesting, probabilistic error model
under which a real number MDS code is shown to correct errors
with probability as compared to for the
more conventional, adversarial error model. We also demonstrate that our
proposed strategy can provide unbounded gains in error tolerance over a
competing replication strategy and a preliminary MDS-code-based strategy for
both these error models.Comment: Presented in part at the IEEE International Symposium on Information
Theory 2018 (Submission Date: Jan 12 2018); Currently under review at the
IEEE Transactions on Information Theor
Performance of cultural heritage institutions: A regional perspective
ProducciĂłn CientĂficaMost studies on performance evaluation in the cultural sector are based on the efficiency assessment of a network of institutions. Nevertheless, very few works take territorial divisions as the case study. Under this approach, we design a spatial production function which merges several cultural resources in order to optimize the impact of a regional system of cultural institutions in terms of cultural production and use of services provided. The aim of this paper is therefore to evaluate the efficiency of cultural heritage institutions in Spain from a regional perspective. We take regional networks of museums and libraries as emblematic case studies over a long period, from 2002 to 2020. We first apply a dynamic-network DEA model to measure efficiency, which allows the production function to be divided into stages and time intervals, considering inter-reliant inputs between production phases and time lapses. We also apply truncated regression models to study the effect of external variables on regional cultural efficiency, especially those related to socioeconomic conditions in regions, the scope of the cultural and tourist sector, and institutional indicators. Results show that regional cultural efficiency depends on the level of training and on the demographic structure rather than on economic wealth. Differences are also found between the goals of cultural production and cultural consumption (visitor impact). These findings might prove useful for policy implications regarding resource allocation vis-Ă -vis defining and accomplishing cultural purposes at a regional scale, and also for revealing causes of inefficiency with a view to improving quality in institutions âwhich ultimately drives economic development
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