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Proceedings Paper

Using distributed genetic algorithms in three-dimensional bin packing for rapid prototyping machines
Author(s): James E. Lewis; Rammohan K. Ragade; Anup Kumar; William E. Biles; Ilkka T. Ikonen
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Paper Abstract

Genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficult constraints, and in high state space dimensionality problems. The classic bin-packing optimization problem has been shown to be a NP- complete problem. There are GA applications to variations of the bin-packing problem for stock cutting, vehicle loading, air container loading, scheduling, and the knapsack problem. Mostly, these are based on a 1D or 2D considerations. Ikonen et. al. have developed a GA for rapid prototyping called GARP, which utilizes a 3D chromosome structure for the bin- packing of the Sinterstation 2000's build cylinder. GARP allows the Sinterstation to be used more productively. The GARP application was developed for a single CPU machine. Anticipating greater use of time compression technologies, this paper examines the framework necessary to reduce GARP's execution time. This framework is necessary to speed-up the bin-packing evaluation, by the use of distributed or parallel GAs. In this paper, a framework for distribution techniques to improve the efficiency of GARP, and to improve the quality of GARPis solutions is proposed.

Paper Details

Date Published: 9 October 1998
PDF: 9 pages
Proc. SPIE 3517, Intelligent Systems in Design and Manufacturing, (9 October 1998); doi: 10.1117/12.326944
Show Author Affiliations
James E. Lewis, Univ. of Louisville (United States)
Rammohan K. Ragade, Univ. of Louisville (United States)
Anup Kumar, Univ. of Louisville (United States)
William E. Biles, Univ. of Louisville (United States)
Ilkka T. Ikonen, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 3517:
Intelligent Systems in Design and Manufacturing
Bhaskaran Gopalakrishnan; San Murugesan, Editor(s)

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