文章摘要
杜佳奇,杨旭东,孙栋,张磊,王晋冰.基于遗传粒子群动态聚类算法的物流柔性分拣系统品规分配[J].包装工程,2024,45(5):126-134.
DU Jiaqi,YANG Xudong,SUN Dong,ZHANG Lei,WANG Jinbing.Genetic Particle Swarm-based Dynamic Clustering Algorithm for Logistics Flexibility Sorting System of Specification Allocation[J].Packaging Engineering,2024,45(5):126-134.
基于遗传粒子群动态聚类算法的物流柔性分拣系统品规分配
Genetic Particle Swarm-based Dynamic Clustering Algorithm for Logistics Flexibility Sorting System of Specification Allocation
投稿时间:2023-09-20  
DOI:10.19554/j.cnki.1001-3563.2024.05.015
中文关键词: 品规分配  品规相似系数  惯性权重因子  遗传粒子群动态聚类算法
英文关键词: product specification distribution  similarity coefficient of specifications  inertia weight factor  genetic particle swarm dynamic clustering algorithm
基金项目:贵州省烟草公司贵阳市公司科技项目(黔烟筑科(2022)1号);贵州省普通高等学校青年科技人才成长项目(黔教合KY字[2021]268)
作者单位
杜佳奇 贵州大学 机械工程学院贵阳 550000 
杨旭东 贵州大学 机械工程学院贵阳 550000 
孙栋 贵州理工学院贵阳 550000 
张磊 贵州省烟草公司贵阳市公司贵阳 550000 
王晋冰 贵州省烟草公司贵阳市公司贵阳 550000 
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中文摘要:
      目的 针对目前烟草物流配送中心条烟分拣量大,不同条烟品规的分配对订单的总处理时间影响较大的问题,研究平衡各个分拣区品规的分配,提高分拣效率。方法 建立以各分区品规相似系数和最小为目标函数的数学模型,并采用改进的遗传粒子群动态聚类(GAPSO-K)算法进行求解。首先,结合各品规分拣量对品规相似系数进行改进,并将其作为适应度函数;然后在粒子群算法中对惯性权重因子进行改进,使其值可以进行自适应改变;最后,在粒子群动态聚类算法中引入遗传算法中的交叉变异扩大解的搜索范围,基于Matlab对文中的其他算法进行求解对比,求得结果在EM-plant中进行仿真验证。结果 结合某烟草物流配送中心数据仿真验证,利用GAPSO-K算法处理订单的时间为234.5 s,较传统时间大幅度较少,有效提升了柔性物流分拣效率。结论 采用该算法可充分发挥2种算法的优良性,具有更好的收敛性及寻优性,为柔性物流品规分配提供了新思路。
英文摘要:
      In order to solve the problems of the large sorting quantity of cigarette in tobacco logistics distribution center and the great impact of assignment of cigarette specification on the total processing time of orders, the work aims to study the allocation of each sorting zone and improve the sorting efficiency. A mathematical model with the objective function of minimizing the similarity coefficients of specification in each zone was developed and solved by an improved genetic particle swarm dynamic clustering (GAPSO-K) algorithm. Firstly, the similarity coefficient of each specification was improved by combining the sorting quantity of each specification as the fitness function. Then, the inertia weight factor was improved in the particle swarm algorithm so that its value could be changed adaptively. Finally, the cross-variance in the genetic algorithm was introduced in the particle swarm dynamic clustering algorithm to expand the search range of the solution, and the results were compared with the other algorithms based on Matlab. The results were simulated and verified in EM-plant. Combined with the data simulation verification in a tobacco logistics distribution center, the time for processing order with GAPSO-K algorithm was 234.5 s, which was significantly reduced compared with the traditional time, effectively improving the efficiency of flexible logistics sorting. The use of this algorithm can give full play to the goodness of both algorithms, with better convergence and merit-seeking, and provides a new idea for flexible logistics product rule allocation.
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