摘要
在工业及服务系统行业,特别是物流及交通运输系统中经常遇到路径规划问题。该文针对自动化立体仓库单拣选台分层水平旋转货架系统,建立了数学模型,引入基于群集智能的蚁群优化算法解决货物拣选路径规划问题。该方法能够对旋转货架系统存储的货物进行快速拣选,并在全局内找到最优货物拣选路径,求解质量高,计算时间短。在货单条目为40的情况下,该文使用改进的蚁群算法求解最优拣选路径比模拟退火算法减小了1 367.17s,比混合遗传算法节省了533.4 s。实验表明该方法适合求解中小规模货物拣选路径规划问题。
Path planning is encountered in a variety of industrial and service applications,especially in logistics and transportation systems.This paper gives a mathematical model for a single-picking station multi-carousel system in an automated warehouse.An ant colony optimization algorithm was used to solve the goods picking path planning problem.Tests show that the ant colony optimization algorithm can rapidly identity the global optimal goods picking path.For a system with items,the improved ant colony optimization algorithm uses 1 367.17 s less time than the simulated annealing algorithm and 533.4 s less than the hybrid genetic algorithm.Thus,this approach improves the operating efficiency of automated storage/retrieval systems for small-size or medium goods picking path planning problems.
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第z2期1770-1773,共4页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金资助项目(60574010)
关键词
群集智能
蚁群优化算法
旋转货架
路径规划
swarm intelligence
ant colony optimization algorithm
carousel system
path planning
