基于自适应加速因子粒子群优化算法的裁剪分床研究

摘要

A cutting distribution method based on self-adaptive acceleration factor particle swarm optimization (PSO) is designed to solve the problem of cutting distribution by garment.First of all,according to the actual production conditions of the cutting,combined with the order information,setting the sum of the error's square of all model's number in the process of cutting as the target to establish the corresponding optimal mathematical model.After that,adopt the algorithm to solve the model,search the number of laying layers of each bed,Then,the responding optimal ratio of all models are aligned according to the search of the number of laying layers,Get the final number of laying layers and the corresponding ratio as the scheme of cutting distribution.Finally,the effectiveness and a faster convergence rate are proved by experiments.%针对服装行业裁剪优化分床这一难题,设计了一种基于自适应加速因子粒子群优化算法的裁剪分床方法;首先,根据裁剪分床的实际生产条件,结合生产订单信息,以裁剪分床过程中各号型样片数量的误差平方之和为目标,建立相应的优化数学模型;然后,采用该算法对模型进行求解,先对各床的铺布层数进行搜索,再根据各床的铺布层数搜索对应各号型的最优配比,搜索的最终铺布层数和对应的配比作为分床方案;最后,通过实验验证了其有效性,且具有较快的收敛速度.