面向堆垛机路径优化的局部搜索自适应遗传算法  

Local Search Adaptive Genetic Algorithm for Stacker Path Optimization

作　　者：史勤政 王嵩[2] 李冬梅 高岑[2] 田月[2] SHI Qin-Zheng;WANG Song;LI Dong-Mei;GAO Cen;TIAN Yue(University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)

机构地区：[1]中国科学院大学,北京100049 [2]中国科学院沈阳计算技术研究所,沈阳110168

出　　处：《计算机系统应用》2020年第8期230-235,共6页Computer Systems & Applications

摘　　要：为了提高自动化立体仓库的运行效率,针对其中的堆垛机路径调度问题,根据时间、能耗和作业效率建立了堆垛机调度优化模型,提出了一种改进的多目标遗传算法IMOGA.该算法在NSGA-Ⅱ算法的基础上改进了遗传算子,采用了适合问题模型的交叉变异操作,引入了自适应遗传算子,并新增了基于模拟退火思想的局部随机搜索策略.以某氨纶厂仓库堆垛机调度情况进行仿真验证,结果表明,IMOGA算法收敛速度更快,解集的质量更高,在堆垛机调度问题上具有更高的适用性.In order to improve the operation efficiency of the three-dimensional warehouse,aiming at stacker path scheduling problem,a stacking machine scheduling optimization model is established based on the time,energy consumption,and operation efficiency,and an Improved Multi-Objective Genetic Algorithm(IMOGA)is proposed.In IMOGA,genetic operator is improved based on NSGA-Ⅱ,crossover and mutation operations are designed for this model,adaptive genetic operator is introduced,and a local random search strategy based on the simulated annealing is added.The IMOGA is validated through the stacker scheduling situation in a spandex factory warehouse.The results show that convergence speed of IMOGA is faster,the quality of the solution set is higher,and it has higher applicability in stacker scheduling.

关 键 词：自适应遗传算法 堆垛机调度 局部随机搜索 PARETO前沿 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH246[自动化与计算机技术—控制科学与工程]