<title>Using distributed genetic algorithms in three-dimensional bin packing for rapid prototyping machines</title>

Abstract

Genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficultconstraints. and in high state space dimensionality problems. The classic bin-packing optimization problem has beenshown 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 one-dimensional or two-dimensional considerations. Ikonen et. al.' have developed a GA for rapid prototyping called GARP,which utilizes a three-dimensional 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 CPUmachine. Anticipating greater use of time compression technologies, this paper examines the framework necessary toreduce GARP's execution time. This framework is necessary to speed-up the bin-packing evaluation, by the use ofdistributed or parallel GAs. In this paper. a framework for distribution techniques to improve the efficiency of GARP, andto improve the quality of GARPIs solutions is proposed.Keywords: genetic-algorithm. distributed-computing. distributed-genetic-algorithms, three-dimensional bin-packing,rapid-prototyping.