João Carlos Soares de Mello

Evaluation of Brazilian electricity distributors using a Network DEA model with shared inputs

Data Envelopment Analysis (DEA) is a methodology widely used to assess the efficiency of electricity distribution companies. Most papers use the network length as one of the variables in the modeling: some of them as an input, since it accounts for the total electricity distributed, and others as an output, since it consists in the product of the investments made and the geographic dispersion, thus showing different dimensions of the same problem. In order to deal with this situation, this work presents an innovative way to deal with this variable by using a Network DEA model with two stages. In our proposed approach, the network length has been set to be both the output of the first stage (whose input is a rate of the operating costs) and an input for the second stage (whose outputs are number of consumers and total consumption). In addition, factors for sharing the operating costs between the stages are taken into account. For the sake of completeness, two Network DEA models are introduced, namely relational and additive approaches. Finally, the results of both models are presented, compared and discussed.

Evaluation of power plants technologies using multicriteria methodology Macbeth

The generation of electricity is a fundamental process in society. Different electricity generation technologies can influence many aspects of human life in earth. The present study, using the multicriteria methodology Macbeth, aims to consider the economic, technical, environmental and social influence to sort the different sources of power generation from the point of view of an expert in energy.

Analysis of efficiency of biodiesel production in a laboratory scale using a Network Data Envelopment Analisys - DEA methodology

This study aims to evaluate the biodiesel production performance in laboratorial scale, applying the Network Data Envelopment Analysis (DEA) model. This model allows to estimate the efficiency and volumes of different kind of oils used in tests. The input and output variables were obtained experimentally at the Laboratory of Post-Harvest Technology and Processing of Agricultural Products - LTPC of the Department of Engineering of the Federal Fluminense University. It was made an efficiency analysis in networks, separated in three stages. In the first stage, were used as inputs: oil, alcohol, catalysts and glycerol (undesirable output) in order to analyze the crude biodiesel production (first stage output). In the second stage, the intermediate product generated in the first stage, was used as input, in addition to acid and deionized water, having as output the washed biodiesel. And, in the third stage, the intermediate product generated in the second stage, washed biodiesel, was used as input, with the final product being the washed and filtered biodiesel. The results suggested that for the production stage, the first one, the smaller volumes were more efficient, but for the global stage the most efficient was the DMU Canola 200mL. The model presented scale returns and a raising scale factor, for all kinds of oil.

Productivity Analysis and Variable Returns of Scale: DEA Efficiency Frontier Interpretation☆

Abstract The main objective of this paper is to analyze DMUs efficiency from the perspective of variable returns to scale. Thus, a case study is proposed, where the efficiencies of DMUs suffer variation according to the methods used in the analysis. The classic models of DEA, CCR and BCC, and a new model proposed by the authors, will have their results compared to classical foundations of the economy. The case study will examine the efficiency of administrative units selected of Undergraduate Higher Education.

Efficiency in Increasing Returns of Scale Frontier

The main objective of this paper is to analyze DMUs efficiency from different perspectives of variable returns to scale. For that, the authors use results of CCR, BCC and a new model proposed. The new model presents just in-creasing returns to scale. Thus, the efficient frontier must have specifics characteristics that guarantees increasing additional output beyond additional inputs verified. This study is going to show that the classic model of DEA BCC proposes variable returns of scale, however cannot ensure that all the efficient DMUs with increasing returns to scale is identified. The authors propose a new model where the measurement of efficiency is made by increasing returns to scale frontier.

Preface to the Special Issue on Decision Support Systems

The XVI CLAIO (Congresso Latino-Iberoamericano de Investigación Operativa) and the XLIV SBPO (Simpósio Brasileiro de Pesquisa Operacional) have been jointly organized and took place in Rio de Janeiro in September 2012. They are the two major events of Operational Research in Latin America and Brazil, respectively. Given the large number of papers dealing with Decision Support, thought began to be given to publish a special issue of the International Transactions in Operational Research devoted to Decision Support Systems (DSS), open to both attendees and non-attendees of the two above mentioned conferences. Although a strict definition of a DSS could limit the number of articles that would cover a “computer-based information system that supports business or organizational decision-making activities”, a more generic DSS definition could be adopted that would comprehend most subjects in decision support. The adopted definition, more comprehensive, is that “a DSS is any system that might support decision making”. Although computational systems are generally used, with this definition DSS no longer focus only on them and may even deal with purely theoretical themes. In fact, of the nine articles in this issue, three are essentially theoretical, one deals with a case study methodology, and the other five are case studies. The studied methodologies are as varied as the themes themselves. They include multicriteria analysis, multiobjective optimization, data envelopment analysis, financial analysis, data mining, and discrete event analysis. The first article of this issue deals with the theoretical properties of some compromise solutions reached by using a social choice function. The second introduces a new tool to assist on the study of pairwise judgments matrices, mainly in the revision of their consistency. The third article deals with the high number of criteria in a problem and how to reduce this number. The fourth article deals with the aggregation of soft systems methodology and discrete-event simulation applied to the study of a real production line. The next articles deal mostly with case studies. The fifth deals with a case study of verbal decision analysis. The sixth makes use of multiobjective programming, genetic algorithms, and geographic information systems to determine the location of clinics. The seventh uses data envelopment analysis (DEA) and fractional regression to estimate the more influential factors in farming efficiency in Brazil. The eighth paper in this issue studies investment planning in the Brazilian oil industry. The last paper makes use of data mining multivariate statistics to detect and rank procedure failures.