面向煤炭企业自动化立体仓库的改进SAGA货位优化  

Improved SAGA Location Optimization for Automated Warehouse in Coal Enterprises

作　　者：曹春玲[1] 邵杨 何龙龙 吴悦[1] 曹龙 CAO Chunling;SHAO Yang;HE Longlong;WU Yue;CAO Long(School of Mechanical Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;Material Supply Company,Shaanxi Energy Liangshuijing Mining Co.,Ltd.,Shenmu 719319,China)

机构地区：[1]西安科技大学机械工程学院,西安710054 [2]陕西能源凉水井矿业有限责任公司物资供应公司,陕西神木719319

出　　处：《煤炭技术》2023年第10期190-194,共5页Coal Technology

基　　金：国家自然科学基金青年科学基金项目(52204174);国家自然科学基金面上项目(520740210);中国博士后科学基金72批面上资助项目(2022MD723828);陕西省自然科学基础研究计划项目(2023-JCQN-0418)。

摘　　要：针对煤矿企业中建成的自动化立体仓库物资供应效率低,货架稳定性差等问题,提出一种基于改进的SAGA算法处理货位分配问题的方法。首先引入了3种不同的货位分配原则,建立货位分配优化模型;其次,为了抑制遗传算法(GA)易陷入局部最优的现象,引入基于Sigmoid曲线的自适应交叉变异操作和逆转操作,之后完成与模拟退火算法(SA)的融合。最终通过仿真实验分析,对改进SAGA进行算法全局搜索性能、收敛性测试,将所得目标函数的最小值与货位随机分配的初始值进行对比,证明了改进SAGA对货位优化问题的优越性,对提高煤炭企业仓储的效率提供了决策方法。Aiming at the problems of low material supply efficiency and poor shelf stability of automated three-dimensional warehouses built in coal mining enterprises, a method based on the improved SAGA algorithm is proposed to deal with the problem of cargo space allocation. Firstly, three different space allocation principles are introduced to establish a cargo space allocation optimization model. Secondly, in order to suppress the phenomenon that the genetic algorithm(GA) is prone to local optimum, an adaptive cross-mutation operation and reversal operation based on Sigmoid curve are introduced, and then the fusion with the simulated annealing algorithm(SA) is completed. Finally,through simulation experiment analysis, the algorithm global search performance and convergence test of the improved SAGA are carried out, and the minimum value of the obtained objective function is compared with the initial value of random allocation of cargo location, which proves the superiority of improved SAGA for location optimization problem, and provides a decision-making method for improving the storage efficiency of coal enterprises.

关 键 词：自动化立体仓库 货位分配 自适应 遗传模拟退火算法 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]