Research On Multi AGV Path Optimization For Non Traditional Warehouse Layout Based On Improved Whale Optimization Algorithm

With the continuous development of China’s manufacturing industry,intelligent logistics plays an increasingly important role in social production.As a transit station in the logistics industry,improving warehouse operation efficiency can effectively reduce enterprise costs and improve the response efficiency of the logistics industry.Using AGV instead of manpower to perform warehouse picking operations can effectively improve warehouse operation efficiency.This paper takes Fishbone and Flying-v non-traditional warehouse layouts as research objects,establishes a multi AGV collaborative picking path optimization model,and improves the Whale Optimization Algorithm to solve and analyze the model.Firstly,according to the characteristics of two non-traditional warehouse layouts,the storage location numbering rules and warehouse layout are designed.Next,model the two warehouse layouts,simulate actual picking scenarios,and construct a location distance matrix about the distance between any two location points.Secondly,based on reasonable assumptions,and by improving the traditional time window and introducing AGV load constraints,a multi AGV picking path model with time windows and AGV load constraints is established,which aims at the total picking distance of multiple AGVs;Then,in view of the slow convergence speed and easy to fall into local optimization problems of classical whale optimization algorithms,corresponding improvement measures are proposed: introducing a chaotic reverse learning strategy for population initialization,enriching population diversity,and accelerating the convergence speed and exploration ability of the algorithm;Improve the linear convergence factor to avoid the problem of poor local search ability caused by linear changes in the convergence factor in the later stage of the algorithm;Introducing a multi headed whale leadership mechanism,with three whales leading the population to update the location to avoid the algorithm falling into a local optimal solution;A sine cosine selection strategy is introduced to randomly perturb the algorithm to enhance its global search ability.Six TSP problems were selected from the TSPLIB database and solved using the improved Whale Optimization Algorithm,and compared with other intelligent optimization algorithms to verify that the improved algorithm outperforms other algorithms;Finally,using Matlab language for simulation,when the order size is 20,40,60,or 80,the improved Whale Optimization Algorithm(TCSWOA),MPGA,IAACO,and CPSO are used to solve the multiple AGV picking path models under the Fishbone and Flying-v warehouse layouts,respectively.Through experimental verification,the TCSWOA solution model has the best effect,indicating that the TCSWOA algorithm is suitable for solving the multiple AGV collaborative picking problem under the Fishbone and Flying-v warehouse layouts.