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Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem

Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem

Julliany Sales Brandão, Alessandra Martins Coelho, João Flávio V. Vasconcellos, Luiz Leduíno de Salles Neto, André Vieira Pinto
Copyright: © 2011 |Volume: 2 |Issue: 1 |Pages: 15
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781613505533|DOI: 10.4018/jaec.2011010103
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MLA

Brandão, Julliany Sales, et al. "Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem." IJAEC vol.2, no.1 2011: pp.34-48. http://doi.org/10.4018/jaec.2011010103

APA

Brandão, J. S., Coelho, A. M., Vasconcellos, J. F., Neto, L. L., & Pinto, A. V. (2011). Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem. International Journal of Applied Evolutionary Computation (IJAEC), 2(1), 34-48. http://doi.org/10.4018/jaec.2011010103

Chicago

Brandão, Julliany Sales, et al. "Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem," International Journal of Applied Evolutionary Computation (IJAEC) 2, no.1: 34-48. http://doi.org/10.4018/jaec.2011010103

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Abstract

This paper presents the application of the one new approach using Genetic Algorithm in solving One-Dimensional Cutting Stock Problems in order to minimize two objectives, usually conflicting, i.e., the number of processed objects and setup while simultaneously treating them as a single goal. The model problem, the objective function, the method denominated SingleGA10 and the steps used to solve the problem are also presented. The obtained results of the SingleGA10 are compared to the following methods: SHP, Kombi234, ANLCP300 and Symbio10, found in literature, verifying its capacity to find feasible and competitive solutions. The computational results show that the proposed method, which only uses a genetic algorithm to solve these two objectives inversely related, provides good results.

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