摘要
针对异型立体化仓库(即建造在洞库内的立体化仓库)物资拣选车任务规划问题进行研究。这一类立体仓库物资拣选存在跨区域、跨巷道作业的可能,因此增加了拣选车拣选任务规划的难度。本文采用自适应遗传算法对建立的数学模型进行优化求解,避免了传统遗传算法常见的早熟收敛,解决了相对复杂情况下的异型立体化仓库物资拣选车任务规划问题。仿真结果表明,该算法所得到的规划方案逼近最优解。
This paper covers task planning of picking machine in Z type high -rise warehouse, one built in cave. Materials in this kind of warehouses are distributed in different area and laneways, so it is more difficult for the picking machine to complete picking task. In this paper, self - adaptive genetic algorithm is used to optimize the built mathematic model and the obtained result is quite approximate to the optimum solution. The method provided in this paper avoids premature eonstringency of traditional genetic algorithm and solves the above picking task planning problem well.
出处
《起重运输机械》
2009年第6期44-47,共4页
Hoisting and Conveying Machinery
关键词
洞库立体化仓库
拣选车
任务规划
自适应遗传算法
早熟收敛
high - rise warehouse in cave
picking machine
task planning
self - adaptive genetic algorithm
premature eonstringency
