基于改进K-均值算法的零部件拣选聚类模型

Components and Parts Picking Cluster Model Based on Improved K-means Algorithm

针对零部件多种少量拣选问题,在多人协同拣选模式下,任务分配不合理、拣选时长相差大,拣选环节易超时,构建以最短拣选时长为目标的多人协同拣选模型,并用改进K-均值算法及遗传算法对模型进行求解。针对传统K-均值算法聚类结果各簇所包含拣选点数量相差巨大的缺点,采用各簇拣选时间为指标,对拣选点所归属簇变换,并利用遗传算法对聚类结果进行路径规划、拣选时长计算,得到最优聚类结果。以某安防设备生产企业的零部件拣选环节为研究对象,与简单分批得到的拣选时间进行对比,验证了该算法的有效性。

Aiming at a variety of small picking problems of parts,in the multi-person collaborative picking mode,the task allocation is unrea-sonable,the picking time varies greatly,and the picking link is easy to timeout.A multi-person collaborative picking model aiming at the shortest picking time is constructed,and the improved K-means algorithm and genetic algorithm are used to solve the model.Aiming at the shortcomings of the traditional K-means algorithm clustering results,the number of picking points contained in each cluster varies greatly.The picking time of each cluster is used as an index to transform the cluster where the picking points belong.The genetic algorithm is used to per-form path planning and picking time calculation on the clustering results to obtain the optimal clustering results.Taking the parts picking pro-cess of a security equipment manufacturing enterprise as the research object,the effectiveness of the algorithm is verified by comparing with the picking time obtained by simple batching.