摘要
根据物料相关性及需求频率,建立了多巷道仓库中货位分配的优化数学模型。模型的优化目标是,既要尽量将关系密切的物料聚集摆放,又要尽量将需求频率高的物料靠近出入库口存储。为求解这一NP问题,提出一种结合启发式算法的混合遗传算法。数值实验表明,该模型与不考虑需求相关性的分配策略相比,可以得到更好的结果,并且随着需求相关性水平的提高,效果更加明显。
By considering both material relevancy and requirement frequency, an optimization model for the storage location assignment in the multi-aisle warehouse was established. With the model, materials with closer relationships were compelled to be stored together closely, while at the same time, materials with high requirement frequen cy were stored close to the I/O point. A Hybrid Genetic Algorithm （HGA） was proposed to solve the NP problem. Numerical experiments showed that this model could obtain better results than other strategies without considering requirement correlations. As the level of demand correlations increased, the improvement was even more significant.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2008年第12期2447-2451,共5页
Computer Integrated Manufacturing Systems
基金
国家863计划资助项目(2008AA04Z102)~~
关键词
需求相关性
货位分配
模型
遗传算法
料单拣选
demand correlations
storage location assignment
model
genetic algorithm
order picking
