Study On Intelligent Optimization Algorithm For Logistics Warehouse Management Using Insect Intelligent Architecture

The rapid development of the logistics industry puts forward higher demands on the efficiency of warehouse operations.With the diversification of customer requirements and the complexity of warehouse operations,the rapid picking of warehouse enterprises is the key factor for improving operational efficiency and customer satisfaction,and reducing operating costs.Through the analysis of the warehouse operation process,it is found that:(1)The logistics management of the centralized logistics warehouse is low level,which results in low utilization rate and low operation efficiency and low management level.(2)In the distribution stage,there are lots of problems,such as slow cargo turnover,poor shelf stability and low cargo position correlation.(3)There is an optimal path selection problem in the picking of cargo.Based on the insect intelligent architecture and the logistics warehouse management,in the paper,the warehouse location assignment and picking path optimization algorithm are proposed.The research are shown as follows.(1)A logistics warehouse management system is proposed using insect intelligent architecture.The system has the advantages of fast networking,simple configuration,and strong versatility of control algorithms,that improves the intelligent level of warehouse management.(2)According to the problem of slow turnover,poor shelf stability and low cargo space correlation during the distribution process,an improved PMO-PSO(parallelized multi-objective particle swarm optimization)location assignment optimizationalgorithm based on insect intelligent architecture is proposed.The typical multi-objective test function model is taken as an example to prove the effectiveness of the improved algorithm with the PMO-PSO algorithm.(3)For the optimal path selection problem in the picking process,combined with the advantages of the ant colony algorithm and the genetic algorithm,the ant colony algorithm improves the searching ability of the algorithm through the parallelization strategy,and the PAC(Parallelized Ant Colony)parameters with GA(Genetic Algorithm).The problem of parameter setting improves the performance of the algorithm optimization,and at the same time verifies with the typical TSP(Traveling Saleman Problem),indicating the effectiveness of the parameter optimization method.(4)Case study on two problems,the results show that the improved PMO-PSO location allocation optimization algorithm reduces the four targets by 10.37%,30.86%,71.72% and 88.48%,respectively.The performance of warehouse management have been greatly improved,including the goods turnover rate,the center of gravity of the frame,the stability of the frame,and the relevance of the goods.Compared with the traditional traversing strategy,the insect intelligence algorithm with genetically optimized parallel ant colony parameters greatly shortens the picking route by approximate 40% which significantly improves the stocking efficiency.In the paper,the location assignment and picking path optimization algorithm of the logistics warehouse based on the insect intelligent architecture are proposed to solve the problems of low automation level,low utilization rate,low operating efficiency and low management level of the current logistics warehouse management system.It is of great significance for the intelligent management of warehouse management in Chinese logistics industry.