scholarly journals Optimal solutions of multiplier DEA models

2021 ◽  
Author(s):  
Victor V. Podinovski ◽  
Tatiana Bouzdine-Chameeva
Keyword(s):  
Returns To Scale ◽  
Scale Production ◽  
Optimal Solutions ◽  
Optimal Input ◽  
Input And Output ◽  
Decision Making Unit ◽  
Dea Models ◽  
Constraint Structure ◽  
Conventional Models ◽  
Almost All

AbstractConventional models of data envelopment analysis (DEA) are based on the constant and variable returns-to-scale production technologies. Any optimal input and output weights of the multiplier DEA models based on these technologies are interpreted as being the most favorable for the decision making unit (DMU) under the assessment when the latter is benchmarked against the set of all observed DMUs. In this paper we consider a very large class of DEA models based on arbitrary polyhedral technologies, which includes almost all known convex DEA models. We highlight the fact that the conventional interpretation of the optimal input and output weights in such models is generally incorrect, which raises a question about the meaning of multiplier models. We address this question and prove that the optimal solutions of such models show the DMU under the assessment in the best light in comparison to the entire technology, but not necessarily in comparison to the set of observed DMUs. This result allows a clear and meaningful interpretation of the optimal solutions of multiplier models, including known models with a complex constraint structure whose interpretation has been problematic and left unaddressed in the existing literature.

Developing an inverse range directional measure model to deal with positive and negative values

2019 ◽  
Vol 57 (9) ◽  
pp. 2520-2540 ◽  
Author(s):  
Sara Yousefi ◽  
Reza Farzipoor Saen ◽  
Seyed Shahrooz Seyedi Hosseininia
Keyword(s):  
Supply Chains ◽  
Cash Flow ◽  
Efficiency Score ◽  
Negative Data ◽  
Flow Management ◽  
Content Type ◽  
Cash Flow Management ◽  
Input And Output ◽  
Decision Making Unit ◽  
Dea Models

Purpose To manage cash flow in supply chains, the purpose of this paper is to propose inverse data envelopment analysis (DEA) model. Design/methodology/approach This paper develops an inverse range directional measure (RDM) model to deal with positive and negative values. The proposed model is developed to estimate input and output variations such that not only efficiency score of decision making unit (DMU) remains unchanged, but also efficiency score of other DMUs do not change. Findings Given that auto making industry deals with huge variety and volumes of parts, cash flow management is so important. In this paper, inverse RDM models are developed to manage cash flow in supply chains. For the first time, the authors propose inverse DEA models to deal with negative data. By applying the inverse DEA models, managers distinguish efficient DMUs from inefficient ones and devise appropriate strategies to increase efficiency score. Given results of inverse integrated RDM model, other combinations of cash flow strategies are proposed. The suggested strategies can be taken into account as novel strategies in cash flow management. Interesting point is that such strategies do not lead to changes in efficiency scores. Originality/value In this paper, inverse input and output-oriented RDM model is developed in presence of negative data. These models are applied in resource allocation and investment analysis problems. Also, inverse integrated RDM model is developed.

FINDING THE REFERENCE SET OF A DECISION MAKING UNIT

2008 ◽  
Vol 25 (04) ◽  
pp. 563-573 ◽  
Author(s):  
G. R. JAHANSHAHLOO ◽  
A. SHIRZADI ◽  
S. M. MIRDEHGHAN
Keyword(s):  
Decision Making ◽  
Returns To Scale ◽  
Optimal Solutions ◽  
Degenerate Problem ◽  
Numerical Examples ◽  
Reference Set ◽  
Decision Making Unit ◽  
The Status

In this paper we propose a method for finding the reference set of a decision making unit (DMU), without chasing down all alternative optimal solutions of the envelopment form, which is a strong degenerate problem. The reference set is useful as a benchmark for an inefficient DMU, for identifying the status of returns to scale, ranking of DMUs and so on. Lastly, numerical examples are shown to illustrate our proposed approach.

scholarly journals A Convex Combination Approach for Artificial Neural Network of Interval Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3997
Author(s):  
Woraphon Yamaka ◽  
Rungrapee Phadkantha ◽  
Paravee Maneejuk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Convex Combination ◽  
Reference Points ◽  
Combination Method ◽  
Suitable Alternative ◽  
Input And Output ◽  
Artificial Neural ◽  
Conventional Models ◽  
Interval Valued

As the conventional models for time series forecasting often use single-valued data (e.g., closing daily price data or the end of the day data), a large amount of information during the day is neglected. Traditionally, the fixed reference points from intervals, such as midpoints, ranges, and lower and upper bounds, are generally considered to build the models. However, as different datasets provide different information in intervals and may exhibit nonlinear behavior, conventional models cannot be effectively implemented and may not be guaranteed to provide accurate results. To address these problems, we propose the artificial neural network with convex combination (ANN-CC) model for interval-valued data. The convex combination method provides a flexible way to explore the best reference points from both input and output variables. These reference points were then used to build the nonlinear ANN model. Both simulation and real application studies are conducted to evaluate the accuracy of the proposed forecasting ANN-CC model. Our model was also compared with traditional linear regression forecasting (information-theoretic method, parametrized approach center and range) and conventional ANN models for interval-valued data prediction (regularized ANN-LU and ANN-Center). The simulation results show that the proposed ANN-CC model is a suitable alternative to interval-valued data forecasting because it provides the lowest forecasting error in both linear and nonlinear relationships between the input and output data. Furthermore, empirical results on two datasets also confirmed that the proposed ANN-CC model outperformed the conventional models.

scholarly journals The Efficiency of Waste Sector in Italy: An Application by Data Envelopment Analysis

2020 ◽  
Vol 24 (3) ◽  
pp. 225-238
Author(s):  
Massimo Gastaldi ◽  
Ginevra Virginia Lombardi ◽  
Agnese Rapposelli ◽  
Giulia Romano
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Performance ◽  
Returns To Scale ◽  
Sustainable Growth ◽  
Waste Reduction ◽  
Data Envelopment ◽  
Environmental Legislation ◽  
Dea Models ◽  
Developed Nations ◽  
Variable Returns To Scale

AbstractWith growing environmental legislation and mounting popular concern for the need to pursuing a sustainable growth, there has been an increasing recognition in developed nations of the importance of waste reduction, recycling and reuse maximization. This empirical study investigates both ecological and economic performances of urban waste systems in 78 major Italian towns for the years 2015 and 2016. To this purpose the study employs the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. More specifically, in the context of environmental performance we implement two output-oriented DEA models in order to consider both constant and variable returns to scale. In addition, we include an undesirable output – the total amount of waste collected – in the two models considered. The results show that there is variability among the municipalities analysed: Northern and Central major towns show higher efficiency scores than Southern and Islands ones.

Uplatnění neparametrické metody DEA při zkoumání efektivnosti obcí a měst

2021 ◽  
Author(s):  
Marek Jetmar ◽  
Jan Kubát
Keyword(s):  
Public Services ◽  
Returns To Scale ◽  
Administrative District ◽  
Data Set ◽  
Average Value ◽  
Dea Models ◽  
Municipal Police ◽  
The Given ◽  
Variable Returns To Scale

The article deals with the application of data envelope analysis (DEA), in examining the efficiency of selected public services provided by municipalities and cities. The method is focused on calculating indicators for individual municipalities and groups of municipalities. When calculating the efficiency, the DEA model with variable returns to scale and superefficiency is used. The distance from the efficiency limit (data envelope) is not measured by Euclidean, as classical DEA models, but by Chebyshev distance. The analysis focuses on examining efficiency within groups of municipalities, defined according to the number of inhabitants and location in relation to development centers, but also these groups in the context of the entire data set. The created model allows to calculate the efficiency of each municipality and monitor its ranking within the given category, but also the type of municipality, administrative district or region. It then shows the practical results of the calculation of efficiency - the achieved average value on the example of schools and municipal police. The variability of the results achieved is subject to interpretation with respect to the services examined. Finally, the limits of DEA use are discussed with regard to the quality of available data and the overall appropriateness of the method for monitoring the efficiency of municipalities.

A Stochastic Frontier Analysis of Exporting Small and Medium Sized Enterprises in India

2016 ◽  
Vol 7 (3) ◽  
pp. 1-23
Author(s):  
Manoj Kumar
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Returns To Scale ◽  
Key Factors ◽  
Technical Inefficiency ◽  
Frontier Analysis ◽  
Efficiency Performance ◽  
Size Firm ◽  
Almost All

This study employs a stochastic frontier analysis (SFA) and technical inefficiency effects model to predict the technical efficiency of 3,168 Indian manufacturing and exporting SMEs, analyze their returns to scale and key factors impacting on their technical efficiency. Indian manufacturing and exporting SMEs extensively rely on labor rather than capital to increase their output, including almost all exporting SME groups, except those exporting to North & South America. The production of Indian manufacturing SMEs exporting to Oceania, however, has increasing returns to scale (1.1965). The inefficiency effects model reveals that firm size, firm age, foreign ownership, location and government assistance are firm-specific factors that significantly affect the technical inefficiency of production. Finally, evidence-based policies are also provided to facilitate improvement in the technical efficiency performance of Indian manufacturing and exporting SMEs.

scholarly journals A Comparison of Ensemble and Dimensionality Reduction DEA Models Based on Entropy Criterion

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 232
Author(s):  
Parag C. Pendharkar
Keyword(s):  
Dimensionality Reduction ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Objective Functions ◽  
High Entropy ◽  
Selection Of Variables ◽  
Dea Models ◽  
Entropy Criterion ◽  
Selection Of ◽  
Normally Distributed

Dimensionality reduction research in data envelopment analysis (DEA) has focused on subjective approaches to reduce dimensionality. Such approaches are less useful or attractive in practice because a subjective selection of variables introduces bias. A competing unbiased approach would be to use ensemble DEA scores. This paper illustrates that in addition to unbiased evaluations, the ensemble DEA scores result in unique rankings that have high entropy. Under restrictive assumptions, it is also shown that the ensemble DEA scores are normally distributed. Ensemble models do not require any new modifications to existing DEA objective functions or constraints, and when ensemble scores are normally distributed, returns-to-scale hypothesis testing can be carried out using traditional parametric statistical techniques.

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