Abstract: Optimal control objectives based on a stochastic storage strategy for an Automated Storage/Retrieval System（AS/RS），in which some spaces are occupied，are defined as the assignment optimizations for the whole warehouse and locations in it，and that for travel time of Storage/Retrieval Machines（SRMs）．In this paper，the controlling strategies of section assignment and location assignment of an automated warehouse are discussed．The mathematic model of the section assignment optimization is built．Based on optimization of section assignment，the mathematic model of the location assignment optimization is built further.Combining the concept of Pareto optimal sets with genetic algorithm，the improved Pareto genetic algorithm for resolving the problems of multi-objective optimization is proposed，which is used to deal with the location assignment.The simulation experiment is given，and the result is analyzed．The problem of location assignment optimization can be effectively resolved via the improved genetic algorithm proposed by this paper．The research result of this paper is valuable for improving the eficiency of automatic warehouse．

Key words: automated storage/retrieval system, Multiobjective Optimization（MOP）, genetic algorithm

摘要： 存储一定数量货物的自动化仓库中，以基于随机存储策略的库区和货位分配以及堆垛机行驶时间为优化控制目标，针对自动化立体仓库的库区和货位的分配策略问题进行了讨论，提出立体仓库的库区优化数学模型。在库区优化基础上，进一步提出货位优化数学模型，将Pareto最优解的概念与遗传算法相结合，提出了一种解决多目标优化问题的Pareto遗传算法解决货位优化问题，给出了仿真实验及分析。结果表明采用遗传算法优化策略可以有效地解决自动化立体仓库的货位优化分配问题。

关键词: 立体仓库, 多目标优化, 遗传算法