基于模拟退火蚁群算法的拣货路径规划

Picking path planning based on Simulated Annealing Ant Colony Algorithm

为优化仓库中多层货架的拣选路径规划,节约拣选时间,提高仓储的运行效率,提出了一种结合了蚁群算法和模拟退火算法的模拟退火蚁群算法,这种算法给人工拣货人员作出了拣货路径的规划。有时仅仅使用一种算法解决复杂的问题效果并不理想,所以将两种算法融合可以避免求解问题过程陷入局部最优,同时减少算法的迭代次数。经过算例仿真,新算法较传统的模拟退火算法,得出最优解需要的时间降低10.7%,较蚁群算法迭代次数减少28.4%,有效提高了人工拣选的效率。

In order to optimize the picking path planning of multi⁃shelf in warehouse,picking time is saved and the operation efficiency of warehouse is improved.A Simulated Annealing Ant Colony Algorithm combining Ant Colony Algorithm(ACA)and Simulated Annealing Algorithm(SAA)is presented in this paper.Sometimes it is not good to rely solely on one algorithm to solve complex problems.The fusion of the two algorithms can avoid the problem solving process falling into the local optimum and reduce the iteration times of the algorithm.After example simulation,compared with the traditional simulated annealing algorithm,the new algorithm reduces the time needed for optimal solution by 10.7%and the number of iterations by 28.4%compared with the ant colony algorithm,which effectively improves the efficiency of manual selection.