【摘要】 优化排样研究是为提高排样效率及材料利用率以增强企业市场竞争力而进行的研究。针对大规模零件优化排样问题,研究了一种混合排样策略和排样解码算法及相应的智能排样算法。该策略对图形处理、同型排样、解码算法、智能算法进行研究,采用在最低水平线法排样解码算法基础上加入分列思想的排样解码算法和在改进遗传算法基础上加入元算法和禁忌搜索算法的方法提高了大规模零件的排样效率。最后给出了混合排样系统的流程与计算实例,验证了算法的可行性。更多还原

【Abstract】 The purpose of large parts optimal nesting study is to enhance the market competitiveness by improving the nesting efficiency and utilization of materials of enterprises.We present a method for improving the nesting efficiency,a layout decoding algorithm and a corresponding intelligent layout algorithm for large-scale layout issues by a mixed layout strategy.This strategy includes the graphics processing,the same type layout,the decoder algorithm and intelligent algorithms for improving the nesting efficiency.We solved the problem of inefficient large parts lay out based on the minimum horizon of nesting decoder algorithm and the improved genetic algorithms that increase calculation efficiency by combining meta algorithm with tabu search algorithm.Finally,giving a mixed layout process as an example,we verify the feasibility of this algorithm.更多还原