Research On Two-dimensional Nesting Based On The Genetic Algorithm

In recent years, the China manufacturing industry has developed rapidly. And the industries of sheet metal, clothing, glass, papermaking and so on are involved in cutting process. The aim of nesting which is based on the limited raw materials is looking for technical and effective ways to cut more parts. Optimal design of nesting can reduce production of cost and enhance core competitiveness of enterprises to maximize economic efficiency of enterprises.First, this article introduces some knowledge of GA(genetic algorithm), and the core idea is to make use of a genetic evolutionary function of chromosomes. During the application of survival of the fittest rule and after several generations of evolution, there may be excellent individuals. Second, the article descrides the genetic algorithm application on nesting, the coding design of chromosomes, initialized population and the termination condition of all kinds of the genetic operators and algorithm and so on.At present, the most commonly used nesting algorithms are BL algorithm, the next step algorithm, the minimum horizontal algorithm and search algorithm based on the lowest horizon. This article describes these algorithm and analyses the effectiveness nesting. Next, the minimum horizontal text search algorithm has been improved and the thresholds have been introduced. It raises a new search algorithm. The algorithm achieves the better ones by searching all the parts and merit-based admissions. Concerned with the selection of the best to discharge, the difference of the sum of the length or width of the two rectangles and a minimum horizontal shold be taken into consideration.At last, the article introduces the idea of coevolution and improves the genetic operators. According to the nesting tree under way, the article designs a new chromosome coding program. The amount of the first chromosomes is the one of the nodes in the first layer of the tree, and the remaining genetic sites represent the amount of the parts that each node contains. In the following, the article introduces the search optimization ability. At last, the article designs a series of optimization system according to the improved genetic algorithm. The system selects some representative nesting data which are tested to have good effect.