###
计算机系统应用:2020,29(8):230-235
本文二维码信息
码上扫一扫!
面向堆垛机路径优化的局部搜索自适应遗传算法
(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)
Local Search Adaptive Genetic Algorithm for Stacker Path Optimization
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 57次   下载 42
投稿时间:2020-01-12    修订日期:2020-02-08
中文摘要: 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.
Abstract: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.
文章编号:7589     中图分类号:    文献标志码:
基金项目:
引用文本:
史勤政,王嵩,李冬梅,高岑,田月.面向堆垛机路径优化的局部搜索自适应遗传算法.计算机系统应用,2020,29(8):230-235
SHI Qin-Zheng,WANG Song,LI Dong-Mei,GAO Cen,TIAN Yue.Local Search Adaptive Genetic Algorithm for Stacker Path Optimization.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):230-235

用微信扫一扫

用微信扫一扫