Armando Zeferino Milioni

Spherical frontier DEA model based on a constant sum of inputs

In this paper, a Data Envelopment Analysis (DEA) model in which a fixed input needs to be assigned to a group of Decision-Making Units (DMUs) is presented. This is performed by assuming the existence of a geometric place represented by a sphere that characterizes the DEA frontier. It is shown that, under this assumption, it becomes relatively easy to find a way to distribute the fixed input to all DMUs, by considering that the individual assignments will be fair through the requirement that all DMUs be efficient or, in other words, be located on the spherically shaped efficiency frontier. A model is presented and results are compared to those obtained by using two different methods proposed in the literature within the same context.

An ellipsoidal frontier model: Allocating input via parametric DEA

This paper presents the ellipsoidal frontier model (EFM), a parametric data envelopment analysis (DEA) model for input allocation. EFM addresses the problem of distributing a single total fixed input by assuming the existence of a predefined locus of points that characterizes the DEA frontier. Numeric examples included in the paper show EFMs capacity to allocate shares of the total fixed input to each DMU so that they will all become efficient. By varying the eccentricities, input distribution can be performed in infinite ways, gaining control over DEA weights assigned to the variables in the model. We also show that EFM assures strong efficiency and behaves coherently within the context of sensitivity analysis, two properties that are not observed in other models found in the technical literature.

Adjusted Spherical Frontier Model: Allocating Input Via Parametric Dea

This paper presents the adjusted spherical frontier model (ASFM), a parametric data envelopment analysis (DEA) model for input allocation. Following a common principle from other solutions found in the literature, ASFM considers that the process of allocating the new input is fair if it ends in such a way that all decision-making units will become DEA-CCR efficient. ASFMs main assumption is the spherical shape of the efficiency frontier. It is because of that assumption that ASFM is called a parametric DEA model. Numeric examples are presented showing that, within the context of sensitivity analysis, ASFM reaches more coherent results than other models found in the literature. This numeric evidence leads to a theorem which formally states this more coherent behaviour. The proof of this theorem is included in this paper.

The Adjusted Spherical Frontier Model with weight restrictions

This paper presents the ASFM-lp model, a parametric Data Envelopment Analysis (DEA) model for allocating resources, commonly called inputs. This model considers that a fair allocation of inputs is one that maximizes the DEA-CCR efficiencies of the Decision Making Units (DMUs). The main assumption of the ASFM-lp is the predefined spherical shape of the efficiency frontier. We have demonstrated that our method extends the existing parametric model ASFM to allow the introduction of weight restrictions, which has great importance in practical applications of DEA. Numeric examples are presented to show the application of the method.

Hyperbolic Frontier Model: A Parametric Dea Approach for the Distribution of a Total Fixed Output

This paper addresses the problem of assigning shares of a new total fixed output to a group of decision making units (DMUs) using data envelopment analysis (DEA), by assuming the existence of a predefined hyperbolic locus of points that characterizes the DEA frontier. The problem of redistributing an already existing output is then addressed, where the total value of this output may vary, so that no DMU is required to decrease its current output value in the new distribution.

On the evalution of Brazilian landline telephone services companies

In this work we investigate the relative efficiency of 34 Brazilian Landline Telephone Service companies using Data Envelopment Analysis with weight constraints in the input and output variables. We formulate two different models that take into account the performance of the companies with respect to the criteria defined by Brazilian National Agency of Telecommunications (ANATEL). We also illustrate the potential of efficiency improvement through the simulation of corporate Merger.

Utilização conjunta de modelagem econométrica e otimização em decisões de concessão de crédito

We use a Logit Model such as the one developed by Scarpel & Milioni (2001), designed to forecast corporations bankruptcy, together with an Integer Programming Model developed by Gehrlein & Wagner (1997). We aim at supporting decisions of credit concession considering the corporations solvency probability estimate and minimizing the sum of opportunity and failure to pay costs. As we show, the conjoint utilization of both models eliminates limitations found in each of them, when used in isolation.

Daily and monthly sugar price forecasting using the mixture of local expert models

This article concerns the application of the Mixture of Local Expert Models (MLEM) to predict the daily and monthly price of the Sugar No. 14 contract in the New York Board of Trade. This technique can be seen as a forecasting method that performs data exploratory analysis and mathematical modeling simultaneously. Given a set of data points, the basic idea is as follows: 1) a Kohonen Neural Network is used to divide the data into clusters of points, 2) several modeling techniques are then used to construct competing models for each cluster, 3) the best model for each cluster is then selected and called the Local Expert Model. Finally, a so-called Gating Network combines the outputs of all Local Expert Models. For comparison purposes, the same modeling techniques are also evaluated when acting as Global Experts, i. e., when the technique uses the entire data set without any clustering.

On the redistribution of existing inputs using the spherical frontier dea model

O Modelo de Fronteira Esferica (MFE) (Avellar et al., 2007) foi desenvolvido para ser usado quando se deseja distribuir de maneira justa um novo insumo a um conjunto de unidades tomadoras de decisao (DMUs, da sigla em ingles, Decision Making Units). A ideia basica do MFE e a de distribuir esse novo insumo de maneira que todas as DMUs sejam colocadas numa fronteira de eficiencia com um formato esferico. Neste artigo, usamos MFE para analisar o problema que surge quando se deseja redistribuir um insumo ja existente para um grupo de DMUs de tal forma que a soma desse insumo para todas as DMUs se mantenha constante. Tambem analisamos o caso em que essa soma possa variar.

Um método para estimativa de metas DEA

The productivity evaluation of commercial units belonging to a single group, such as the branches of a bank or the units of a franchising network, done through Data Envelopment Analysis (DEA), yields efficiency values for each of the units, called DMUs (Decision Making Units). However, this efficiency is relative, for its value depends on how the other DMUs behaved. Since the analysis is done after production, it is not possible to know how much one unit should have produced to be classified as efficient. This work suggests a method for estimating goals of production for each DMU to be considered efficient independently from the rest of the group, from the idealization of an efficiency frontier.