Research On Optimal Model And Algorithm Of Equipment Maintenance Support Inventory

Intelligent intelligence of complex system maintenance resource optimization is undoubted by current and future effects of improved maintenance resource allocation.Equipment repair spare parts As an important maintenance resource,it not only affects the timeliness of equipment repair,but also an important factor affecting the effectiveness of equipment maintenance and protection.The inventory management of maintenance spare parts has become one of the largest investment aspects in equipment maintenance support work.Therefore,the research on the inventory decision optimization model and algorithm of multi-variety spare parts is of great significance.According to the practice of equipment support,this paper studies the optimization of equipment maintenance support inventory decision.The specific research contents are as follows:1)In order to effectively solve the uncertainty of the maintenance spare parts of vehicle equipment,the management cost is too high,the inventory control is unreasonable,and the vehicle equipment maintenance spare parts inventory maintenance model for useless plans is constructed.First of all,the characteristics of spare parts consumption under the two maintenance modes of scheduled maintenance and natural random fault maintenance of vehicle equipment during the planning period are analyzed,and the spare parts consumption model of vehicle equipment is established.Then,considering the replenishment cost,storage cost,space constraint,weight constraint and satisfaction rate constraint of spare parts,the joint replenishment inventory optimization model of multi-variety spare parts under periodic inspection strategy is constructed.The structure of the model is analyzed in detail,the limits of decision variables are determined,and the search space of the algorithm is significantly reduced,based on which an improved genetic algorithm solving model is designed.The simulation results show that the maintenance spare parts consumption is closely related to the use of the application plan.The inventory decision plan solved can not only avoid bad phenomenon such as stock pressure or serious out of stock,but also effectively reduce stock costs of spare parts.2)In order to effectively solve the problems such as the incompatibility between warehouse location and demand point and the disharmony between supply and actual demand,a joint location-inventory optimization method based on hybrid fruit fly optimization algorithm was proposed.First,consider the distributed inventory system consisting of m species maintenance spare parts,1 base warehouse,n candidate base warehouse and q maintenance activity demand points.The joint optimization model of warehouse location-inventory is established by using synchronous replenishment and distribution order strategy.Then,according to the structural characteristics of the model,the location update mode of the traditional fruit fly optimization algorithm is improved by using the method of multi-population co-evolution,and a hybrid fruit fly optimization algorithm with internal and external two-layer search strategy is designed.The outer search strategy is used to search the warehouse location decision variables,and the inner search strategy is used to search the inventory control decision variables.On the basis of retaining the essence of the fruit fly algorithm,the algorithm solves the problem that the algorithm is easy to fall into the local extreme value,and improves the global optimization ability of the algorithm.The simulation results show that the hybrid fruit fly optimization algorithm has good solution efficiency,and the joint optimization decision-making scheme is conducive to the overall optimization of the inventory system and effectively reduce the total operating cost of the inventory system.