Research On Optimization Of Warehouse Allocation Problem Based On Improved Genetic Algorithm

With the rapid development of global modern logistics industry,every enterprise must consider not only the growing needs of users,but also the operating costs of the enterprise,so the storage location assignment problem in warehouse management is paid more and more attention by enterprises.In the process of storage assignment,there are many factors will affect the whole warehouse's efficiency.For example,the stability of the shelves,the location of goods,the path of goods' deliveries and so on.Enterprises should balance all aspects to deal with the relationship among the various factors,and then maximize the profit.Because logistics enterprises have accumulated a lot of historical data,we can apply the idea of data mining to the distribution problem of goods,and the goods are arranged according to the frequency of the delivery of the goods,so as to improve the efficiency of delivery of goods.In this thesis,the mathematical model is established for the storage location assignment problem of the warehouse,the model is a multi-objective optimization problem and considers three factors: shelf stability,delivery time,association rules between goods.In this kind of search problems,the most commonly used algorithm is the genetic algorithm,but the algorithm is easy to fall into the local optimal solution,and the simulated annealing algorithm can jump out of local optimal solution with a certain probability.In this thesis,the characteristics of the simulated annealing algorithm is used to optimize the traditional genetic algorithm,the improved genetic algorithm is combined with the object function to assignment the goods.The experimental result shows that the improved genetic algorithm is better than the traditional genetic algorithm,improving the efficiency of the whole warehouse and reducing the operation cost of the enterprise.