Research On The Optimization Of Production Materials JIT Distribution Of Assembly Enterprise

In the production process of assembly manufacturing enterprise,involving a large number of parts and components,resulting in the production line,especially assembly line material is difficult to deliver just in time.Accurate and timely material distribution is helpful to improve the production efficiency of the workshop.This paper focuses on the optimization of production materials JIT distribution of assembly enterprises.The following research is carried out:Firstly,according to the characteristics of the assembly enterprise,the present situation and the existing problems of the material distribution are analyzed,and the significance of the research is expounded.Then,the idea of pulling production before the process is choosed.Production mode can improve the waste of business and inventory waste,so that the production mode from extensive production to lean production changes.Secondly,the paper focuses on the material distribution in the assembly process,how to carry out the material distribution to the production line after the goods are carried out,and comes down with the path planning problem with deadline and minimize the total transportation cost.Improved ant colony algorithm is used to optimize the problem.Through the dynamic updating of pheromone and the improved design of the heuristic function,the algorithm is adaptive and overcomes the shortcomings of traditional ant colony algorithm in the process of traversing optimization process prone to stagnation and premature convergence of the shortcomings.The vehicle loading of goods and stacker picking path planning is the premise of JIT distribution center operations.On the one hand,the mathematic model of stacker picking operation is established,and the continuous particle swarm optimization(PSO)algorithm is used to optimize the stacking path of the stacker in the roadway.On the other hand,a mathematical model of equalization of vehicle load and volume is established,and a genetic discrete particle swarm optimization(PSO)algorithm is established for the characteristics of the discrete combination of cargo assembly in the buffer zone.These two methods add the crossover and mutation evolution strategies of the basic genetic algorithm.The particles find the optimal value by means of the intersection of individual extremes and group extremes and the variation of the particles themselves.Finally,the factor analysis method is used to construct the evaluation system,and the index system which affects the transportation service level of the carrier is determined.The data distribution is evaluated by the statistical analysis of the data,and the adverse factors are improved.