中文摘要：

针对单规格一刀切二维矩形排样问题,提出了一种启发式搜索算法,称为大小工件分治择优匹配（bigitem smallitem divide-and-conquer best-fit,简称BSDBF）启发式算法.该算法基于组化规则,提出了大小工件分治策略和组块快速举荐算法,是对组化策略的关键补充,这对优解获得至关重要.然后,择优选择适应度高的组块进行递归排样,贪心获得各块板材的排样方案.最后,基于设计的工件拆分方法,对初始解进行后处理小规模重排,进一步提升解的质量.因为没有随机因素,其获得的优解可复现,也是BSDBF算法区别于其他算法的典型特征.大量Benchmark案例的实验结果表明,BSDBF算法求解质量优于其他算法的报道结果.

英文摘要：

This paper proposes a novelty heuristic search algorithm, called BSDBF （bigitem smallitem divide-and-conquer best-fit）, to solve the two-dimensional rectangular fixed-size guillotine bin packing problem. First, based on group rules, this algorithm implements big item smalltime divide-and-conquer strategy and efficient group recommendation scheme which are key points to improve the group strategy. Then, the best-fit group is selected for recursive packing, and packing solution is achieved greedily for all bins. Finally, an initial solution is obtained, and a post processing algorithm is used to improve the quality of the solution based on item splitting method. That the solution can be obtained again is the critical characteristic of BSDBF algorithm which is different from others algorithms, because there is not any random factor in BSDBF algorithm. The computational results of many Benchmark problems have shown that BSDBF algorithm outperforms others reported algorithms.