摘要
研究仓储路径规划对智能仓储具有重要意义,合理的路径规划能够有效避免仓储路径冲突,提升仓库内货物运输效率。针对当前仓储布局较为简单、缺乏针对复杂仓储布局的路径冲突策略问题,提出基于AGV坐标保留表和冲突分类的多目标AGV路径规划算法。首先,构建基于网格法的智能仓储鱼骨布局方案,并根据分区机制,给出存储节点间的距离计算模型,构成单一单向的仓储路径网络有向图。其次,建立AGV坐标保留表方法和路径冲突分类方法,制定路径冲突解决策略和算法。然后,建立以最小化总运输距离、最小化最大运输距离、最小化冲突解决等待时间为目标的多目标智能仓储路径规划模型。最后,结合所提路径冲突解决算法,设计基于进化遗传搜索算法的突变操作,在基于学习的多目标组合优化求解算法P-MOCO的基础上,通过构建偏好条件随机策略,借助多目标降维和强化学习方法,提出改进P-MOCO的无冲突多目标智能仓储路径优化算法CF-MOWVRP,求解无冲突的多目标规划模型的近似帕累托解。实验结果表明,所提算法具备更快的收敛速度和更优的解,能够解决路径冲突,给出无冲突的路径规划方案。
The research on warehouse path planning plays a crucial role in intelligent warehousing,as reasonable path planning can effectively avoid AGV path conflicts and improve in-warehouse transportation efficiency.To address the limitations of simplistic warehouse layouts and the lack of effective path conflict resolution strategies for complex environments,this paper proposes a multi-objective AGV path planning algorithm based on a coordinate reservation table and conflict classification.Firstly,a grid-based fish-bone layout scheme for intelligent warehousing is constructed.A distance calculation model between storage nodes is developed using a partition mechanism,forming a unidirectional directed graph representing the storage path network.Next,an AGV coordinate reservation table and a path conflict classification method are established,followed by the formulation of a hierarchical conflict resolution strategy.Then,a multi-objective intelligent warehouse path planning model is constructed with the goals of minimizing the total transportation distance,minimizing the maximum single transportation distance,and minimizing the waiting time for conflict resolution.Based on the proposed conflict resolution mechanism,a set of mutation operators and crossover operations is designed under an evolutionary genetic search framework.On top of the preference-guided multi-objective combinatorial optimization(P-MOCO)algorithm,an enhanced algorithm named CF-MOWVRP is proposed.This algorithm integrates preference-driven stochastic strategies,multi-objective dimensionality reduction,and reinforcement learning to obtain approximate Pareto-optimal solutions to the conflict-free multi-objective path planning model.Experimental results demonstrate that the proposed algorithm achieves faster convergence and better solution quality,successfully resolves AGV path conflicts,and provides feasible conflict-free path planning solutions.
作者
宫婧
杨玉发
郑一帆
孙知信
GONG Jing;YANG Yufa;ZHENG Yifan;SUN Zhixin(Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology),Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Modern Postal College,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《计算机科学》
北大核心
2026年第4期88-100,共13页
Computer Science
基金
国家自然科学基金(62272239)
江苏省农业科技自主创新项目(CX(22)1007)
贵州省科技支撑项目([2023]一般272)。
关键词
路径规划
智能仓储
路径冲突
多目标优化
强化学习
AGV
Path planning
Intelligent warehousing
Path conflict
Multi objective optimization
Reinforcement learning
AGV
