Using distributed genetic algorithms in three-dimensional bin-packing for rapid prototyping machines

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 one-dimensional or two-dimensional considerations. Ikonen et. al.(1) 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 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.