Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach

This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in convectional DEA is that inputs and outputs data are measured with precision. However, production agriculture takes place in an uncertain environment and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data is known to lie within specified intervals and empirically illustrate this approach using a group of 34 dairy producers in Pennsylvania. Compared to the convectional DEA scores that are point estimates, the computed fuzzy efficiency scores allow the decision maker to trace the performance of a decision-making unit at different possibility levels.fuzzy set theory, Data Envelopment Analysis, membership function, α-cut level, technical efficiency, Farm Management, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty, D24, Q12, C02, C44, C61,

A possibilistic approach to latent structure analysis for symmetric fuzzy data.

In many situations the available amount of data is huge and can be intractable. When the data set is single valued, latent structure models are recognized techniques, which provide a useful compression of the information. This is done by considering a regression model between observed and unobserved (latent) fuzzy variables. In this paper, an extension of latent structure analysis to deal with fuzzy data is proposed. Our extension follows the possibilistic approach, widely used both in the cluster and regression frameworks. In this case, the possibilistic approach involves the formulation of a latent structure analysis for fuzzy data by optimization. Specifically, a non-linear programming problem in which the fuzziness of the model is minimized is introduced. In order to show how our model works, the results of two applications are given.Latent structure analysis, symmetric fuzzy data set, possibilistic approach.

A geometrical approach for fuzzy DEA frontiers.

Interval DEA frontiers are here used in situation where one input or output is subject to uncertainty in its measurement and is presented as an interval data. We built an efficient frontier without any assumption about the probability distribution function of the imprecise variable. We take into account only the minimum and the maximum values of each imprecise variable. Two frontiers are constructed: the optimistic and the pessimistic ones. We use fuzszy relationships to introduce a new efficiency index based on a set of some Fuzzy T Norms. We will explore only the case where only on single variable presents a certain degree of uncertainty

Regression Models for Market-Shares

On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretation of the parameters in relation to models for the total sales based on discrete choice models. Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model

The role of multiplier bounds in fuzzy data envelopment analysis

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The non-Archimedean epsilon ε is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of ε can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs when the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to re-shape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach

Modelos flexibles para la valoración de la eficiencia

El objetivo en esta Memoría ha sido el análisis de eficiencia de un determinado sector empresarial, teniendo en cuenta dos problemas casi siempre presentes, y de naturaleza muy diferente, por una parte, que los datos que se manejan pueden ser imprecisos y, por tanto, afectar al resultado de cualquier estudio de eficiencia y, por otra parte, el deseo de ordenar las empresas (Unidades De Toma de Decisión) atendiendo a la medición de su eficiencia. Para la medición de la eficiencia se ha recurrido a la metodología no paramétrica del Análisis Envolvente de datos (DEA) aplicandola a empresas del sector textil muy cercanas a nosotros. Ahora bien, dado que consideramos que siempre existe alguna incertidumbre o un posible error en la medición de algunos datos (inputs y outputs), introducimos la limitación de la certeza con el tratamiento fuzzy de los datos, métodos que no requieren conocer ni aplicar hipótesis sobre distribuciones de probabilidad de esos datos, que dicho sea de paso, podría no ser fáctible bajo determinados supuestos de incertidumbre. Pero además de la medir la eficiencia pretendemos proporcionar más información que la mera separación dicotómica entre empresas eficientes o no eficientes. Para ello desarrollamos y aplicamos los modelos de super-efficiencyfuzzy y cross-efficiency-fuzzy, que nos permiten establecer una ordenación bajo incertidumbre. Con este trabajo hemos realizado un estudio amplio de la eficiencia bajo incertidumbre. Se observa que los resultados obtenidos aplicando los distintos métodos son similares. Además, estos métodos proporcionan más información sobre las unidades estudiadas que las que proporciona un solo índice de eficiencia. Estos métodos pueden ser aplicables a otros tipos de empresas, aportando nueva información que puede ayudar u orientar en la toma de decisiones de sus gestoresPla Ferrando, ML. (2013). Modelos flexibles para la valoración de la eficiencia [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31521TESI