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Pesquisa Operacional, Volume: 32, Número: 2, Publicado: 2012
  • Rough set and rule-based multicriteria decision aiding

    Slowinski, Roman; Greco, Salvatore; Matarazzo, Benedetto

    Resumo em Inglês:

    The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems.
  • Differentiated risk models in portfolio optimization: a comparative analysis of the degree of diversification and performance in the São Paulo Stock Exchange (BOVESPA)

    Gartner, Ivan Ricardo

    Resumo em Inglês:

    Faced with so many risk modeling alternatives in portfolio optimization, several questions arise regarding their legitimacy, utility and applicability. In particular, a question arises involving the adherence of the alternative models with regard to the basic presupposition of Markowitz's classical model, with regard to the concept of diversification as a means of controlling the relationship between risk and return within a process of optimization. In this context, the aim of this article is to explore the risk-differentiated configurations that entropy can provide, from the point of view of the repercussions that these have on the degree of diversification and on portfolios performance. The reach of this objective requires that a comparative analysis is made between models that include entropy in their formulation and the classic Markowitz model. In order to contribute to this debate, this article proposes that adaptations are made to the models of relative minimum entropy and of maximum entropy, so that these can be applied to investment portfolio optimizations. The comparative analysis was based on performance indicators and on a ratio of the degree of portfolio diversification. The portfolios were formed by considering a sample of fourteen assets that compose the IBOVESPA, which were projected during the period from January 2007 to December 2009, and took into account the matrices of covariance that were formed as from January 1999. When comparing the Markowitz model with two models that were constructed to represent new risk configurations based on entropy optimization, the present study concluded that the first model was far superior to the others. Not only did the Markowitz model present better accumulated nominal yields, it also presented a far greater predictive efficiency and better effective performance, when considering the trade-off between risk and return. However, with regards to diversification, the Markowitz model concentrated its weights in only five of the fourteen sample assets. Contrary to these two models, the maximum entropy model showed a level of diversification that was very close to the maximum level, which would be a situation that is far more in keeping with Markowitz's diversification precepts. However, these models showed the worst results in the comparative analysis of performance.
  • Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

    Oliveira, Sandra C.; Andrade, Marinho G.

    Resumo em Inglês:

    In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.
  • Application of an iterative method and an evolutionary algorithm in fuzzy optimization

    Silva, Ricardo Coelho; Cantão, Luiza A.P.; Yamakami, Akebo

    Resumo em Inglês:

    This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
  • A new method for decision making in multi-objective optimization problems

    Augusto, Oscar Brito; Bennis, Fouad; Caro, Stephane

    Resumo em Inglês:

    Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.
  • Decision aiding in plastic surgery: a multicriteria analysis

    Gomes, Luiz Flávio Autran Monteiro; Rangel, Luís Alberto Duncan; Fernandes, Priscila Pereira

    Resumo em Inglês:

    The aim of this article is to present, through a real case, a practical way, based on Multicriteria Decision Aiding, to support decision making in Plastic Surgery. The case studied was a Caucasian woman of 36 years of age with mammarian hypertrophia with ptosis and abdominal lipodystrophy, making it necessary to select the most adequate techniques for the best aesthetic result. For this purpose, the multicriteria methods Even Swaps and PrOACT were used. Three plastic surgeons working in the city of Rio de Janeiro with equivalent professional experience were consulted as decision agents. In order to define the objectives to be achieved, the criteria relevant to the making of the decision and the alternatives which could be used were identified. Throughout this identification and in the later analysis the surgeons participated in the application of the methods, which contributed towards facilitating their acceptance of the multicriteria analysis in their decision making. It was confirmed, in this case study, that the use of Multicriteria Decision Aiding tends to make the medical decision more wide ranging and, above all, transparent. The plastic surgeons themselves validated the analysis, considering it fully consistent with their professional experience.
  • Unitary input DEA model to identify beef cattle production systems typologies

    Gomes, Eliane Gonçalves; Abreu, Urbano Gomes Pinto de; Mello, João Carlos Correia Baptista Soares de; Carvalho, Thiago Bernardino de; Zen, Sérgio de

    Resumo em Inglês:

    The cow-calf beef production sector in Brazil has a wide variety of operating systems. This suggests the identification and the characterization of homogeneous regions of production, with consequent implementation of actions to achieve its sustainability. In this paper we attempted to measure the performance of 21 livestock modal production systems, in their cow-calf phase. We measured the performance of these systems, considering husbandry and production variables. The proposed approach is based on data envelopment analysis (DEA). We used unitary input DEA model, with apparent input orientation, together with the efficiency measurements generated by the inverted DEA frontier. We identified five modal production systems typologies, using the isoefficiency layers approach. The results showed that the knowledge and the processes management are the most important factors for improving the efficiency of beef cattle production systems.
  • Validating rankings in soccer championships

    Sant'Anna, Annibal Parracho; Mello, João Carlos Correia Baptista Soares de

    Resumo em Inglês:

    The final ranking of a championship is determined by quality attributes combined with other factors which should be filtered out of any decision on relegation or draft for upper level tournaments. Factors like referees' mistakes and difficulty of certain matches due to its accidental importance to the opponents should have their influence reduced. This work tests approaches to combine classification rules considering the imprecision of the number of points as a measure of quality and of the variables that provide reliable explanation for it. Two home-advantage variables are tested and shown to be apt to enter as explanatory variables. Independence between the criteria is checked against the hypothesis of maximal correlation. The importance of factors and of composition rules is evaluated on the basis of correlation between rank vectors, number of classes and number of clubs in tail classes. Data from five years of the Brazilian Soccer Championship are analyzed.
  • Using ELECTRE TRI to support maintenance of water distribution networks

    Trojan, Flavio; Morais, Danielle Costa

    Resumo em Inglês:

    Problems encountered in the context of the maintenance management of water supply are evidenced by the lack of decision support models which gives a manager overview of the system. This paper, therefore, develops a model that uses, in its framework, the multicriteria outranking method ELECTRE TRI. The objective is to sort the areas of water flow measurement of a water distribution network, by priority of maintenance, with data collected from an automated system of abnormalities detection. This sorting is designed to support maintenance decisions in terms of the measure more appropriate to be applied per region. To illustrate the proposed model, an application was performed in a city with 100 thousand water connections. With this model it becomes possible to improve the allocation of maintenance measures for regions and mainly to improve the operation of the distribution network.
  • Some experiments with a savings heuristic and a tabu search approach for the vehicle routing problem with multiple deliverymen

    Ferreira, Vanessa de Oliveira; Pureza, Vitória

    Resumo em Inglês:

    In this work we consider a variant of the vehicle routing problem that allows the assignment of multiple deliverymen to one or more routes. A practical motivation for this variant arises, for example, in the distribution of beverages in highly dense urban areas, characterized by the difficulty in serving daily requests within regular working day hours with a single deliveryman per vehicle. We present a mathematical model and a savings algorithm in order to generate low cost routes that maximize the number of requests served in compliance with the maximum route time. The impact of the extra deliverymen on the solutions provided by the proposed heuristic is assessed by means of sets of generated examples based on classical instances of literature. It is also presented the results obtained by an adaptation of a tabu search approach from the literature.
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