Comparative Research On Location Optimization Of Particle Swarm Optimization And Grey Wolf Algorithm

With the advent of the industry 4.0 era,the market demand is more and more diversified.If all enterprises want to be eliminated in the fierce competition,they must adapt to the trend of development and introduce advanced production and manufacturing models.The current national policy is vigorously supporting enterprises' scientific and technological innovation,and Chinese manufacturing industry is gradually moving toward the era of intelligent manufacturing.The domestic land cost and labor cost are rising continuously.The traditional logistics and warehousing system can no longer meet the needs of enterprises.As one of the most advanced warehousing technologies,the automatic warehouse has begun to receive the attention of enterprises.The automatic warehouse has a long history,and has been widely used in various industries due to its advantages of high space utilization,high efficiency in storage and storage,and fast turnover.There are also many researches on the optimization of automatic warehouses at home and abroad.Among them,the research on cargo space optimization can be used by the majority of scholars to make full use of warehouse space and improve warehouse efficiency without increasing the investment in warehouse hardware facilities.At present,the research on the optimization of automatic three-dimensional warehouse location is more mature.Most of the researches have the lowest shelf center of gravity and the fastest turning speed to establish a mathematical model to solve this problem.PSO,GA and SA have many applications in the location optimization problem and the effect is better.The Grey Wolf algorithm is a new intelligent algorithm that is not used for location optimization problems.In this thesis,the particle swarm optimization algorithm with good effect and the grey wolf algorithm are applied to the same location optimization model.In the thesis,it combines the high-temperature static storage of lithium batteriesand the properties of stored goods to mathematically model the location optimization model.And the three-dimensional warehouse model is divided into warehouses according to the rows,columns and layers of the shelves.The automated warehouse is regarded as a three-dimensional rectangular space,and each cargo space is represented by its row,column and number of layers.(1)According to the principle of location optimization turnover rate,the objective function one has been established with the sum of all the goods turnover rate and the time of the inbound and outbound time that need to be optimized.(2)According to the minimum principle of center of gravity optimization,the objective function two has been established with the sum of the product quality of all goods to be optimized and the product of the number of layers.(3)According to the anti-overturning principle of the shelf,the objective function three has been established by minimizing the difference in the weight of the goods stored on the two sides of the single lane.(4)Finally,the analytic hierarchy process is used to determine the weights of the three sub-objective functions,and the final objective function is obtained to transform the location optimization problem into a single-objective optimization problem.Finally,according to the principle of particle swarm optimization and grey wolf algorithm,the objective function has been optimized respectively,which proves the feasibility of the algorithm on this problem,and compares the effects before and after optimization,and the advantages of two different algorithms in the optimization of the location.Inferiority.