Study On The Optimization Algorithm Of Fishbone Storage Layout Path With Picking And Depositing Collaboration

Warehousing route optimization plays a pivotal role in the sorting efficiency of warehousing orders,and warehousing optimization is mainly divided into traditional layout path optimization and non-traditional layout path optimization.According to research,fishbone non-traditional layouts have advantages over two-partition traditional layouts in order picking when constraints are relaxed.Based on the fishbone non-traditional layout,this paper studies the order picking path optimization algorithm in the access collaboration mode.Firstly,the characteristics of logistics warehousing layout are analyzed,the fishbone layout can effectively improve picking efficiency,the coding mode and operation principle of warehousing path optimization algorithm are studied,and the characteristics of fast convergence speed of gray wolf algorithm and strong balance ability of local performance and global performance of the algorithm are introduced.The gray wolf algorithm is proposed to solve the problem of fishbone storage path optimization under the constraint of access collaboration.Secondly,according to the layout characteristics of the fishbone bureau warehousing,each cargo point is coded and modeled,so as to establish the fishbone-shaped layout moving distance matrix,take the shortest picking distance as the optimization goal,consider the access coordination and load constraints,and establish the fishbone-type storage layout access collaborative path optimization model.In addition,in view of the shortcomings of the gray wolf algorithm in solving the fishbone path optimization problem,five improvement methods are analyzed,including: nonlinear convergence factor strategy,gray wolf gene genetic strategy,particle swarm renewal strategy,local elimination strategy and greedy update strategy,and a hybrid improved gray wolf algorithm based on particle swarm is given.Finally,through Matlab programming simulation,the optimization ability of the adaptive gray wolf algorithm,the adaptive particle swarm algorithm and the improved gray wolf algorithm proposed in the paper in the five basic test functions is compared,and the advantages of the proposed algorithm are verified,and the effectiveness of the improved algorithm is verified by comparing the picking distance optimization results of the classic gray wolf algorithm without access collaboration,the classic gray wolf algorithm under access collaboration,and the gray wolf improvement algorithm under access collaboration.