基于改进模拟退火遗传算法的自动化立体仓库货位优化  

Optimization of Automatic Storage Location Based on Improved Simulated Annealing Algorithm

作　　者：易斌[1] 敬舒瑶 YI Bin;JING Shuyao(College of Business,Hunan University of Technology,Zhuzhou Hunan 412007,China)

机构地区：[1]湖南工业大学商学院,湖南株洲412007

出　　处：《包装学报》2023年第3期76-84,共9页Packaging Journal

基　　金：湖南省社会科学成果评审委员会课题(XSP22YBC350)。

摘　　要：针对自动化立体仓库出入库作业量大、品类复杂等特点,对仓库货位展开优化研究,遵循周转率原则、货架稳定原则、关联原则等,构建了提高出库效率、提高货架稳定性、增强货物相关性的货位优化模型。使用传统模拟退火算法、遗传算法与改进模拟退火遗传算法求解模型,得出货位优化结果。对比分析结果证明了在解决货位优化问题时,改进算法比传统基础算法更加有效,能更好地改善自动化立体仓库空间使用率低、拣选效率低、货位摆放混乱的现状。In response to the characteristics of large workload and complex categories in automated three-dimensional warehouses,the optimization research was conducted on warehouse storage spaces.Following the principles of turnover rate,shelf stability,and correlation,a storage space optimization model was constructed to improve outbound efficiency,shelf stability,and cargo correlation.A comparative analysis was conducted using traditional simulated annealing algorithm,genetic algorithm,and improved simulated annealing genetic algorithm to solve the model’s cargo location optimization results.It was proved that the improved algorithm is more effective than the traditional basic algorithm in solving the cargo location optimization problem,which helps to improve the current situation of low space utilization,low picking efficiency,and chaotic cargo placement in automated three-dimensional warehouses.

关 键 词：自动化立体仓库 货位优化 改进算法 

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