【摘要】 在对无重复规格一维下料优化问题数学模型分析的基础上,提出了基于改进遗传算法的优化下料方案求解方法。具体做法是,以实数表示的各零件长度的一个排列作为一个染色体,对一个可能解进行编码,其中的每个零件长度为一个基因;同时,为了便于遗传算子的设计,对染色体的基因进行分段,同一段上的基因表示它们截自同一原材料;通过基于基因分段的杂交、变异获得优化解。实验结果表明该算法是解决无重复规格一维下料问题的可行算法。更多还原

【Abstract】 By analyzing the mathematical model of one-dimensional cutting-stock problem without replicated sizes, this article presents a GA-based method for the cutting-stock problem. The concrete means is to choose a real valued arrangement of the components lengths as a chromosome to code a possibility solution, each components length being a gene. At the same time, in order to facilitate the design of operators, the chromosome genes are divided into sections, genes in the same section indicating that they are cut from same material. Through crossbreeding and variation based on sectionalization of genes the optimization solution is obtained. The experiment results show that the algorithm proposed in this article is efficient.更多还原