A Research On Optimized Methods For Industry Explosives Storage

Along with our country economic development rapidly develop, the demand of industrial explosive greatly increased, with the demand the industrial industry has greater developed. Due to the particularity of the industrial industry,and for a variety of factors, the industry has been hit by a regulatory of strict control, in the explosive storages management has a more strict requirements. At present the domestic explosive dangerous goods warehouses are serious shortage, the existing explosives warehouse management is not standard, there are some problem such as security hidden danger. It is great significance to improve the utilization rate of the warehouse of explosive industry development through enhancing the automation of explosives warehouse, reduceing workers, and optimizing the explosives storage process.Aiming at Some practical problems for process of explosives storage, an on-line optimizing operation method of explosives storage based on the radio frequency identification technology (RFID) and genetic algorithm is presented in the paper. Mainly to complete the following content:(1)The explosives storage model is set up, using genetic algorithm to optimize the problem:This paper shows a novel approach to use the characteristics of explosive warehouse working and requirement analysis to establish the optimizing mathematical model of explosive warehouse. On the basis of the original algorithm,in order to improve the research performance of algorithm and To improve the convergence of the algorithm to avoid algorithm trapped into the limitation of local optimization in the process of researching optimization,based on the crossover rate Pc and mutation rate Pm change within the scope of certain linear..By adopting the adaptive method to set parameter,the algorithm can accord the need to automatic adjust the parameter in the process of the optimization searching, the ability of algorithm can be improved.(2) The explosives storage multi-objective model is established, using the multi-objective genetic algorithm to solve the multi-objective model: To comprehensive consider the explosives production sales and storage,the inventory factor of each explosive was introduced in the explosives warehouse. First, an assignment strategy for explosive warehouse is proposed according to the largest inventory of each explosive, based on the constraint conditions that minimizing the amount of existing inventory in the warehouse,the shortest forklift working hours and as much as possible for the high frequency of explosives stored in near the entrance, to establish a multi-objective optimization mathematic model of explosives storage and to use the multi-objective genetic algorithm to solve the model. Then using the adaptive multi-objective genetic algorithm based on the linear transformation of the crossover rate and the mutation rate to solve the multi-objective optimized problem for explosives storage. The crossover rate and the mutation rate be dynamically selected.The simulation results verify the feasibility of this method, which can improve the warehouse space utilization rate, optimize walk path that the explosive access process and solve the operation optimization problems preferably on the condition that the expire date of explosive is retained. Using the multi-objective genetic algorithm can be able to improve warehouse space utility rate to some extent, At the same time, to ensure the certain amount of inventory of each explosive in the warehouse,it can meet that the explosive production is flexible, explosive sales is more stable, and to a certain extent, optimizing the forklift walk path that the explosive access process and solve the operation optimization problems preferably on the condition that the expire date of explosive is retained. the experimental simulation shows that the improved multi-objective genetic algorithm is better than the ordinary multi-objective genetic algorithm.