Research On Warehouse Picking Optimization Strategy Based On Improved Iterative Local Search Algorithm

Storage optimization is widely used in the field of production and manufacturing.With the promotion of multi variety,small batch production and sales mode,the requirements for the operation efficiency of storage system are higher and higher.Therefore,the research on the optimization of storage system is of far-reaching significance to improve the operation efficiency of storage stacker and reduce the operation cost.According to the order characteristics in the warehouse and the operation characteristics of the stacker,this paper regards it as the vehicle routing problem of prize collection,which belongs to a typical NP hard problem.In recent years,great progress has been made in the research of prize collection vehicle routing problem,the solution efficiency is also improving,and the quality of Pareto frontier solution is also improving.However,in the existing solution strategies,the current solution is easy to fall into local optimization and has poor convergence;It is easy to produce a large number of invalid solutions in the local search stage,resulting in the inability to quickly and effectively obtain the global optimal solution.On this basis,the main research contents of this paper are as follows:(1)In view of the fact that the picking strategy of warehouse stacker is easy to fall into the local optimization problem in the solution process,based on the iterative local search algorithm,a strong disturbance strategy is designed in the disturbance stage to increase the search step of the solution and speed up the convergence speed;At the same time,epsilon constraint is set in the local search stage.In order to prevent the function value of one target direction from becoming significantly worse when optimizing the function of the other target direction,effectively avoid a large number of invalid solutions,improve the operation efficiency of the algorithm,and make the algorithm obtain the global optimal solution more quickly and efficiently.(2)For the picking strategy optimization of warehousing in and out at the same time,when the solution falls into local optimization,if the strong disturbance strategy is used,the search step will be too large and fall into circuitous search.An adaptive strength perturbation strategy is designed,and the search step of the current solution is adaptively determined according to the historical iteration information to improve the convergence speed of the solution;In order to ensure the diversity and distribution uniformity of the solution space,a random intensity disturbance strategy is designed to make the search direction of the solution space more global.Finally,in order to further improve the local search ability,a collaborative optimization algorithm of hybrid iterative local search and variable neighborhood search is designed for optimization.Through the experiment with the comparison algorithm on the benchmark test set,the results show that it has obvious advantages in the number of non-dominant solutions,the hypervolume and spacing metric.