INTELCSP: computational intelligence applied to cutting stock problems

Authors:
Rodrigo Rabello Golfeto;Antônio Carlos Moretti;Luiz Leduí-no De Salles Neto
Affiliations:
Production Engineering Department, Fluminense Federal University, Avenida dos Trabalhadores, 420, Vila Santa Cecí-lia, Volta Redonda, RJ, 27255-970, Brazil.;Institute of Mathematics, Statistics and Scientific Computation, State University of Campinas, Cidade Universitária Zeferino Vaz, s/n, Barão Geraldo, Campinas, SP, 13084-790, Brazil.;Department of Science and Technology, Federal University of São Paulo, Avenida Mário Covas, 610, Vila Nair, São José dos Campos, SP, Brazil
Venue:
International Journal of Computational Intelligence Studies
Year:
2012

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Abstract

This study presents the promising results obtained for an intelligent decision-making system for industrial processes in which the cutting stock problem is a component relevant to production planning. In order to establish a cutting process assisted by an intelligent system, with memory and learning capabilities, we utilised a symbiotic genetic algorithm (Symbio) that we developed for cutting stock problems with multiple objectives, setup costs and waste. We used case-based reasoning (CBR) as a learning strategy. The results obtained show that this is a promising approach.