Research On The Optimization Of Enterprise Distribution Network Based On Genetic Algorithm

In the process of economic integration, the traditional distribution network model can no longer meet the diverse needs of the market. The distribution network quality affects the interests of enterprises in the market competition. Companies need to design the rational distribution network to meet the customer demands to stay be in an invincible position.In enterprise management, inventory optimization is very important. In the whole inventory storage management, inventory control is an significant issue. On the basis of customer service meeting the requirements, inventory control is to control the various manufacturers, distribution centers and distribution points' inventory levels, improve the market competitiveness when the inventory levels are as low as possible.This paper concerns two objective function optimization models, the total system cost and delivery time. Firstly, through a detailed analysis of Optimization strategy of distribution network and various cost factors, this paper discusses the inventory policy at all levels of the nodes and proposes a distribution network model which is based on location-inventory-transportation. Secondly, using the improved genetic simulated annealing algorithm in the model, the variables in the model are encoded by different encoding mode in which way, the storage space of chromosome can be reduced. According to different encoding mode, this paper chooses two-point crossover and uniform crossover arithmetic method which are suitable to the rational design of a distribution network model of genetic manipulation.Taking into account that the genetic algorithm is easy to fall into local optimal solution,defecting such slow convergence, this paper uses simulated annealing algorithm with the feature of strong local search capability, remedying these defects of genetic algorithm. the researchers found that the model algorithm are better, faster. Finally, apply the model to a specific numerical example, we selected genetic algorithm and improved genetic simulatedannealing algorithm to solve it, using MATLAB mathematical software to realize the solving process, analysis and comparison of optimization results, verified that the optimization models and improved algorithm has certain practical application value.