摘要
Abstract
To address the challenging issue of operation scheduling path optimization for automated warehouse stackers,an improved Whale Optimization Algorithm(WOA)is proposed.By analyzing the characteristics of the stacker's operational movements,a mathemati-cal model for stacker scheduling path optimization is constructed.Building upon the Whale Optimization Algorithm,a crossover strategy from the Genetic Algorithm(GA)is introduced to enhance the diversity of solutions.Furthermore,a nonlinear convergence factor and dy-namic weighting are employed to strengthen global search capabilities.The algorithm also integrates a Simulated Annealing(SA)ap-proach,leveraging its probabilistic acceptance mechanism to effectively escape local optima.The proposed algorithm is then applied to solve the constructed mathematical model.Simulation data demonstrates that the algorithm achieves satisfactory convergence accuracy for the proposed model,with improvements of approximately 47.59%,21.01%and 38.16%in convergence precision compared to WOA,SA,and GA,respectively.After testing with varying task quantities and insertion scenarios,the algorithm consistently yields superior results,which holds significant research value for enhancing operational efficiency of stackers,reducing electricity and financial costs,as well as improving the throughput and operational benefits of the entire automated warehouse.关键词
自动立体仓库/堆垛机/鲸鱼优化算法/模拟退火算法Key words
Automatic stereo warehouse/Automated stacking crane/Whale optimization algorithm/Simulated annealing algorithm分类
信息技术与安全科学
