| Warehouse management system(WMS) is a specific form of warehouse management informatization, it is an important part of the modern logistics industry. Research found that,a large number of small and medium-sized enterprise warehouse management are still in the state of artificialization, and have no application of warehouse management system, which greatly hindered the development of ningbo small and medium-sized enterprises. This article choose ningbo A enterprise, as the representative of the small and medium enterprises,on the basis of research A enterprise warehouse management present situation and the production requirements, refer to the PHP scripting language and Mysql server, designed a set of warehouse management system which suitable for small and mediumsized enterprise. System including "warehouse management", "warehouse management", "production management", "inventory management", "rights management" five function modules.After warehouse management system running in enterprise, A follow-up found is the position information of system are not be fully used, this reduces the application effect of warehouse management system. So in view of the goods distribution in warehouse, in this paper, through the two aspects of loading and unloading efficiency and shelf stability o testablish the multi-objective optimization model, and use genetic algorithm(GA) to position optimization solution in the form of binary code into chromosomes, after some algebra crossover mutation to get optimal chromosome which is the optimal allocation. Simulation on the MATLAB platform, verify the effectiveness of genetic algorithm in optimization problems. However under the influence of crossover and mutation feature algorithm itself,the convergence speed of genetic algorithm in the optimization process is slow.Particle swarm optimization(PSO) through information sharing between iteration to complete the particle update,so PSO has the advantages of fast convergence speed, and also has the disadvantage of local convergence.Particle swarm optimization algorithm is applied to the discrete problem is mainly used to solve the traveling salesman problem(TSP), which used to solve optimization problems are less researched. In this article, through reference transposition thinking change the rules of updating the particle swarm reduction, and mixed with the simulated annealing algorithm which has the characteristics of probabilistic kick,put forward an innovative algorithm twice update particle swarm algorithm(TUPSO). Simulation on the MATLAB platform, twice update particle swarm optimization algorithm is verified in the effectiveness of slotting optimization problem. Compared with genetic algorithm, found that when twice update particle swarm optimization has similar optimize result with genetic algorithm, not only the convergence algebra of TUPSO ahead GA of the 83 generation, but also the range of goods distribution of TUPSO expanded to 16 area than GA. |
