Hybrid Approach for Optimal Nesting Using a Genetic Algorithm and a Local Minimization Algorithm

Abstract

Abstract In layout design problems including blank nesting, the positions and directions of layout elements must be determined so as to minimize the total space. It is difficult and computationally time-consuming to find the optimal solution for such layout problems, because they include a lot of underlying combinational conditions. In this paper, we present an approach for optimal nesting by combining a genetic algorithm and a local minimization algorithm. In the approach, the genetic algorithm is used for handling the combinations which are represented in the string, and the local minimization algorithm is used for determining the embodiment layout under the fixed combinations so as to minimize the scrap volume which is corresponding to the fitness value in the genetic algorithm. And we present an example for showing the effective nesting result produced by this approach.