Large-scale nesting of irregular patterns using compact neighborhood algorithm

The typical nesting technique that is widely used is the geometrical tilting of a single pattern or selected cluster step by step from the original position to an orientation of 180°, i.e. orthogonal packing. However, this is a blind search of best stock layout and, geometrically, it becomes inefficient when several pattern entities are involved. Also, it is not highly suitable for handling patterns with a range of orientation constraints. In this paper, an algorithm is proposed which combines the compact neighborhood algorithm (CNA) with the genetic algorithm (GA) to optimize large-scale nesting processes with the consideration of multiple orientation constraints.