基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。

An Optimization of Multi-vehicle Picking Routing Problem Based on Fishbone Warehouse Layout

In order to promote the application of fishbone warehouse in actual scenarios,aiming at the problem of picking route optimization under the fishbone warehouse layout,a distance calculation model for picking points and a picking route optimization model based on the shortest multi-vehicle picking distance with load and volume restrictions as the overall goal were constructed.Considering the strong global search ability of the genetic algorithm(GA),the fast convergence speed of the particle swarm optimization(GAPSO)and the strong local optimization ability of the ant colony algorithm(ACO),a hybrid algorithm to solve the optimization model of the picking route was proposed.Through simulation experiments of different order sizes,it is concluded that the hybrid algorithm is superior to the GA algorithm and the GAPSO algorithm in terms of fitness value,number of iterations,and convergence speed.And when the order size is large,the average fitness value is reduced by about 8%,which effectively shortens the total picking distance.The results verify the advancement and effectiveness of the hybrid algorithm in solving the picking route problem under the fishbone warehouse layout,and provide new solutions and ideas for solving the picking route problem inside such warehouses.