机电工程技术2025，Vol.54Issue(2)：100-105,6.DOI:10.3969/j.issn.1009-9492.2025.02.020
多货箱机器人任务调度优化研究
Research on Optimization of Task Scheduling for Multi Container Robots
摘要
Abstract
With the booming development of e-commerce,the demand for intelligent warehousing systems to improve system efficiency and flexibility is increasing.Therefore,based on the background of intelligent warehousing,the task scheduling problem of container robots is studied.By analyzing the characteristics of container robots and the process of task scheduling,the task scheduling problem of multi container robots is decomposed into two sub problems:task grouping,task assignment,and task sorting.Taking into account factors such as robot utilization,time,and distance,a task scheduling optimization model is established for multi container robots in a grid storage environment.The genetic algorithm is improved from three directions:encoding methods for dealing with multi decision problems,improving initial population quality,and improving algorithm convergence.An improved genetic algorithm solution model is designed combining clustering algorithm grouping strategy.A comparative experiment is designed for multiple task grouping strategies under different task scales.The experimental data showes that compared to the strategy clustering algorithm under grouping strategies,the optimization results of the other two grouping strategies are higher by 120%to 200%,proving the superiority of the clustering algorithm grouping strategy.Through simulation experimental data of different algorithms,it is found that the optimization results of the improved genetic algorithm are reduced by 12.27%compared to the genetic algorithm,verifies the effectiveness of algorithms and models in improving warehousing efficiency.关键词
货箱机器人/任务调度/改进遗传算法/聚类算法/任务分组Key words
container robot/task scheduling/improved genetic algorithm/clustering algorithm/task grouping分类
信息技术与安全科学引用本文复制引用
李园园,雷斌,王喜红..多货箱机器人任务调度优化研究[J].机电工程技术,2025,54(2):100-105,6.基金项目
国家自然科学基金资助项目(72061021) （72061021）
甘肃省自然科学基金资助项目(21JR7RA284) （21JR7RA284）
