RCMeans: A Recursive Capacitated Means for Districting Problem

Autores

  • Polyana Bezerra Costa Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Italo Francyles Santos da Silva Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Pedro Henrique Carvalho Vieira Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Robert Douglas Araújo Santos Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Mayara Gomes Silva Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Christyellen Souza Costa Lima Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Daniel Lima Gomes Júnior Universidade Federal do Maranhão, Núcleo de Computação Aplicada, São Luís-MA, Brasil
  • Eliana Márcia Garros Equatorial Energia, Companhia Energética do Maranhão, São Luís-MA, Brasil
  • Italo Fernandes Serra da Silva Equatorial Energia, Companhia Energética do Maranhão, São Luís-MA, Brasil
  • Lucas Paula Assunção Pinheiro Equatorial Energia, Companhia Energética do Maranhão, São Luís-MA, Brasil

DOI:

https://doi.org/10.26729/jadi.v4i1.2745

Resumo

The billing process of an energy distributor in Brazil is connected to reading energy consumption logistics. An efficient, balanced and capacity enabled process has benefits with cost reducing and quality perception of the service provided. In capacitated clustering, the elements are associated with weights for construction of groups with limited capacities. This paper presents an approach to the capacitated clustering problem, applied to the consumer unit measurement groups organization in Brazil's energy distributors companies. The process of creating those measurement groups, in general, is carried out manually by expert analysts. The purpose of this problem modality is to create partitions that minimize the internal dispersion of the associated group. In this work, the RCMeans method, which is based on the K-Means technique applied to data groupings with the inclusion of the capacity constraint for the group definition, is presented. The obtained results show a comparison between the current situation and the result with the proposed method, under the cohesion analysis, separation, number of groups, silhouette index, and consumer units measurement mean time of the groups.

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Publicado

2018-10-18