改进ICA求解自动化立体仓库货位分配问题  

Improve ICA to Solve the Problem of Automatic Warehouse Space Allocation

作　　者：陈兴安 吴超华[1] 王磊[1] 刘文长 CHEN Xing-an;WU Chao-hua;WANG Lei;LIU Wen-chang(School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070)

机构地区：[1]武汉理工大学机电工程学院,湖北武汉430070

出　　处：《制造业自动化》2025年第2期86-95,共10页Manufacturing Automation

基　　金：国家自然科学基金(51905396,52375510)。

摘　　要：为提高自动化立体仓库的货位分配效率,保障其安全稳定运行,建立提高仓库出入库效率、货架稳定性、货物相关性和货物剩余价值为目标的货位优化模型,运用层次分析法将多目标问题转化为单目标问题进行研究。针对该模型提出了一种改进帝国竞争算法,该算法融合了帝国竞争算法与遗传算法的优点,设计了动态调节革命率公式和自然灾变算子增强了殖民地和帝国的多样性。实验结果表明改进帝国竞争算法具有更优的收敛性和搜索范围,有效的解决了不同规模的货位分配问题,求解精度和稳定性优于粒子群算法、遗传算法和标准帝国竞争算法,对提升快销品企业竞争力和立体仓库出入库效率提供了理论依据和实践参考。In order to improve the efficiency of cargo space allocation in automated warehouse and ensure its safe and stable operation,a cargo space optimization model is established to improve the efficiency of warehouse entry and exit,shelf stability,cargo correlation and surplus value of goods.The analytic hierarchy process is used to transform the multi-objective problem into a single-objective problem.An improved imperialist competitive algorithm is proposed for this model.The algorithm combines the advantages of imperialist competitive algorithm and genetic algorithm and designs a dynamic adjustment revolution rate formula and a natural disaster operator to enhance the diversity of colonies and empires.The experimental results show that the improved imperial competition algorithm has better convergence and search range,and effectively solves the problem of location allocation of different scales.The accuracy and stability of the solution are better than those of particle swarm optimization,genetic algorithm and standard imperial competition algorithm.It provides a theoretical basis and practical reference for improving the competitiveness of fast-selling enterprises and the efficiency of warehouse entry and exit.

关 键 词：自动化立体仓库 帝国竞争算法 遗传算法 货位分配 剩余价值率 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F252[自动化与计算机技术—控制科学与工程] F206[经济管理—国民经济]