Genetic algorithms for solving 2D cutting stock problem

Cutting material from stock sheets is a challenging process in a number of important manufacturing industries such as glass industry, textile, leather manufacturing and the paper industry. Basically, it means some smaller parts that have to be cut from a given stock sheet, in such a way, that the waste is minimum. The classical solution methods to solve this problem generally count on the amount of calculations and it is complex to formulate and impossible to solve in some cases. In order to overcome the drawbacks of the classical methods, genetic algorithm (GA) is used to handle the cutting problem. In this paper we solve this problem by three ways. First, approximate the parts using bounding rectangles. Second approximate the parts using the suitable bounding primitive shape (rectangle, triangle, and circle). Finally, with no approximation of the given parts, we introduce some enhancements in the GA to help it to escape from local minima. Also, we study the effect of layout sequence of the cut parts from the sheet on the GA performance.