作　　者： (刘万强）; (周亚勤）; (杨建国）; (尤祥）;

机构地区： 东华大学机械工程学院,上海201620

出　　处： 《东华大学学报（自然科学版）》 2017年第4期496-502,共7页

摘　　要： 从提升有轨自动化小车(RGV)出入库作业效率的角度出发,在综合考虑RGV加减速和行走方向改变所带来的影响前提下,对新型棋盘格密集仓库出入库复合作业模式下的货位分配问题进行研究,设计了基于混合遗传算法的出入库货位分配算法,以对货位分配问题进行求解.仿真试验结果证明,该算法能有效解决不同规模下新型棋盘格密集仓库的货位分配问题,使RGV的整体作业效率提升40%左右,并且具有货架规模越大则效率提升越明显的优势. In order to improve the efficiency of rail guided vehicle （RGV） in warehousing operation, allocation assignment has been carried out for the new checkerboard Intensive warehouse with the consideration of the influence of RGV acceleration and deceleration. Allocation assignment algorithm based on nested genetic algorithm is designed to solve the allocation problem. Simulation experiments show that the algorithm can effectively solve the allocation problem of checkerboard warehouse under various scales, so that the overall operating efficiency of RGV increased by about 40%, and the larger the shelf size, the more obviously the efficiency increases.

关 键 词： 棋盘格密集仓库 货位分配 遗传算法 有轨自动化小车