摘要
针对窄巷道密集仓储系统中叉车的拣选作业三维路径规划问题,通过分析该系统中的拣选作业流程,考虑拣选叉车实际运动中速度的动态变化和容量限制,建立以拣选作业时间最小化为目标的数学模型。在标准粒子群算法的基础上,引入遗传算法中的变异交叉机制,并结合变邻域搜索、动态惯性权重等寻优策略,设计一种改进的混合粒子群算法来求解该模型。以某配送中心的VNA仓储系统为例,对模型和算法进行验证,并将该算法与GA和PSO两种智能算法进行比较,实验结果表明:该算法具有更好的优化性能和求解精度,其对三种不同规模任务量的拣选作业时间优化比例分别为21.2%、24.7%和26.7%,能有效地减少窄巷道仓储系统的拣选作业耗时,从而提升系统的拣选作业效率。
The three-dimensional path planning problem of forklift picking operation in the very narrow aisle(VNA) dense warehousing system was studied. By analyzing the picking operation process in the system, a mathematical model with the objective of minimizing picking operation time was established with consideration of dynamic speed change and capacity limitation in the actual movement characteristics of the picking forklift. Based on the standard particle swarm optimization algorithm, variation cross mechanism in the genetic algorithm was introduced and the optimization strategies such as variable neighborhood search and dynamic inertia weight were combined to design an improved hybrid particle swarm optimization algorithm. Finally, the VNA warehousing system of a distribution center was used as an example to verify the algorithm and model, and the algorithm was compared with GA and PSO intelligent algorithms. The results showed that the algorithm had better optimization efficiency and solution accuracy, and its time optimization ratios for three different scales of picking tasks were 21.2%, 24.7% and 26.7%, respectively, which can effectively reduce the time of picking operations in the VAN warehousing system, thereby improving the system efficiency of picking operation.
作者
周驰
董宝力
ZHOU Chi;DONG Baoli(Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310018,China)
出处
《浙江理工大学学报（自然科学版）》
2020年第6期823-830,共8页
Journal of Zhejiang Sci-Tech University(Natural Sciences)
基金
国家自然科学基金项目(51475434)
浙江省自然科学基金项目(LY14G010007)。
关键词
窄巷道
拣选作业
三维路径规划
密集仓储系统
混合粒子群算法
very narrow aisle(VNA)
picking operation
three-dimensional path planning
dense warehousing system
hybrid particle swarm optimization
