DCA - Departamento de Engenharia de Computação
URI Permanente desta comunidadehttps://repositorio.ufrn.br/handle/1/128
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Navegando DCA - Departamento de Engenharia de Computação por Autor "Aloise, Daniel"
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Artigo Column generation bounds for numerical microaggregation(Springer, 2014-02-18) Rocha, Caroline Thennecy de Medeiros; Aloise, Daniel; Hansen, Pierre; Santi, ÉvertonThe biggest challenge when disclosing private data is to share information contained in databases while protecting people from being individually identified. Microaggregation is a family of methods for statistical disclosure control. The principle of microaggregation is that confidentiality rules permit the publication of individual records if they are partitioned into groups of size larger or equal to a fixed threshold value, where none is more representative than the others in the same group. The application of such rules leads to replacing individual values by those computed from small groups (microaggregates), before data publication. This work proposes a column generation algorithm for numerical microaggregation in which its pricing problem is solved by a specialized branch-and-bound. The algorithm is able to find, for the first time, lower bounds for instances of three real-world datasets commonly used in the literature. Furthermore, new best known solutions are obtained for these instances by means of a simple heuristic method with the columns generatedArtigo Uma ferramenta computacional par o agendamento de operações do programa de acessibilidade especial porta a porta - PRAE(SOBRAPO, 2014) Rocha, Caroline Thennecy de Medeiros; Dantas, Saulo de Tarso Alves; Aloise, Daniel; Galvão, José Claudio; Martins, Ana Maria da SilvaEm todo o mundo, a demanda por serviços de transporte para pessoas portadoras de necessidades especiais, idosos, e pessoas com mobilidade reduzida vêm crescendo nos últimos anos. A população está envelhecendo, os governos precisam se adaptar a esta realidade, e este fato pode significar oportunidade de negócios para as companhias. Dentro deste contexto está inserido o Programa de Acessibilidade Especial porta a porta – PRAE do município de Natal-RN. A pesquisa presente neste trabalho procura desenvolver um modelo de programação capaz de auxiliar o processo de tomada de decisão dos gestores deste serviço de transporte. Para tanto, foi criado um algoritmo baseado em métodos de geração de soluções aproximativas conhecidas como heurísticas. O objetivo do modelo é incrementar o número de pessoas atendidas pelo PRAE, dada a frota disponível, gerando programações de roteiros otimizadas. O PRAE consiste em um problema de roteirização e programação de veículos do tipo dial-a-ride – DARP, um dos tipos mais complexos dentre os problemas de roteirização. A validação do método de resolução foi feita mediante a comparação entre os resultados auferidos pelo modelo computacional e a programação manual real atual. Os resultados mostraram que o modelo idealizado neste trabalho foi capaz de elevar a capacidade de atendimento deste serviço de transporteArtigo Global optimization workshop 2012(Springer, 2014-07-24) Rocha, Caroline Thennecy de Medeiros; Aloise, Daniel; Hansen, PierreThe Global Optimization Workshop 2012 (GOW 2012) was the eleventh of a series of meetings, organized to be a forum for both academic and industrial communities to present and discuss the latest results and challenges in global optimizationArtigo A model for clustering data from heterogeneous dissimilarities(Elsevier, 2016-09-16) Santi, Éverton; Aloise, Daniel; Blanchard, Simon J.Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many applications, more than one instance of the dissimilarity matrix is available and so to conform to model requirements, it is common practice to aggregate (e.g., sum up, average) the matrices. This aggregation practice results in clustering solutions that mask the true nature of the original data. In this paper we introduce a clustering model which, to handle the heterogeneity, uses all available dissimilarity matrices and identifies for groups of individuals clustering objects in a similar way. The model is a nonconvex problem and difficult to solve exactly, and we thus introduce a Variable Neighborhood Search heuristic to provide solutions efficiently. Computational experiments and an empirical application to perception of chocolate candy show that the heuristic algorithm is efficient and that the proposed model is suited for recovering heterogeneous data. Implications for clustering researchers are discussedArtigo A review of dynamic data envelopment analysis: state of the art and applications(Wiley, 2017-11-01) Mariz, Fernanda B. A. Rocha; Almeida, Mariana Rodrigues de; Aloise, DanielThis article reports the evolution of the literature on Dynamic Data Envelopment Analysis (DDEA) models from 1996 to 2016. Systematic searches in the databases Scopus and Web of Science were performed to outline the state of the art. The results enabled the establishment of DDEA studies as the scope of this article, analyzing the transition elements to represent temporal interdependence. The categorization of these studies enabled the mapping of the evolution of the DDEA literature and identification of the relationships between models. The three most widely adopted studies to conduct DDEA research were classified as structuring models. Mapping elucidated the literature behavior through three phases and showed an increase in publications with applications in recent years. The analysis of applications indicated that most studies address evaluations in the agriculture and farming, banking and energy sectors and consider the facilities as transition elements between analysis periodsArtigo A simple and effective genetic algorithm for the two-stage capacitated facility location problem(Elsevier, 2014) Fernandes, Diogo Robson Montes; Rocha, Caroline Thennecy de Medeiros; Aloise, Daniel; Ribeiro, Glaydston M.; Santos, Enilson Medeiros dos; Silva, AllysonThis paper presents a simple and effective Genetic Algorithm (GA) for the two-stage capacitated facility location problem (TSCFLP). The TSCFLP is a typical location problem which arises in freight transportation. In this problem, a single product must be transported from a set of plants to meet customers demands, passing out by intermediate depots. The objective is to minimize the operation costs of the underlying two-stage transportation system thereby satisfying demand and capacity constraints of its agents. For this purpose, a GA is proposed and computational results are reported comparing the heuristic results with those obtained by two state-of-the-art Lagrangian heuristics proposed in the literature for the problem