摘要
针对一维优化下料问题,将基于群体的编码方法与遗传算法相结合,设计了一种适用于一维优化下料问题的编码方法,修改了经典遗传算子的操作方法,提出了降序最佳置换方法(BRD).引入最佳配合(BF)、优先配合降序(FFD)局部搜索算法,建立了求解一维优化下料问题的复合遗传算法.应用结果显示,本文方法的效果是令人满意的.
This paper discussed the solution for a general one-dimensional cutting stock problem in which a set number of linear elements like rod are cutting from stock lengths of different sizes so that wastage is minimized. The nature of this problem is such that the traditional approaches of exact methods or approximation are not effective. This paper presents the application of a modified grouping genetic algorithm （GGA） to this grouping problem. The GGA method is enhanced with local optimization techniques such as the best replace decreasing （BRD）,the best fit （BF） and the first fit decreasing （FFD）. The results of studies show the effectiveness and efficiency of this approach for the problems.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2006年第6期1015-1018,1023,共5页
Journal of Shanghai Jiaotong University
关键词
一维下料
优化下料
遗传算法
one-dimension cutting stock
cutting optimization
genetic algorithm （GA）
