Demand Prediction Of Aircraft Spare Parts And Guarantee Strategy Of Two Stages Reseach

With the rapid changes in new technology and the advancement of militarymodernization，the modern aviation equipment had been significantly brought to a new level,highlighting the importance of the integrated supply guarantee system. In regard to theintegrated supply guarantee system, the demand prediction and the allocation optimization ofthe aircraft spare parts are two integral parts. Therefore, how to accurately predict the demandfor aircraft spare parts, implement the optimization strategies on the spare parts in stock in ascientific manner and look for the optimum balance between the support resources and thesupply efficiency of spare parts supply are key issues concerning the research of theintegrated supply guarantee.Firstly, theories related to the integrated supply guarantee system of the aircraft spareparts are introduced and the advantages and disadvantages of the two-level supply guaranteesystem and three-level supply guarantee system are analyzed, the classification of the spareparts is introduced, the currently well-understood methods of demand prediction are presented,the optimization algorithm available is illustrated and the characteristics and procedures of thegenetic algorithm are analyzed.Secondly, based on the analysis of demand generation law of the spare parts, theimproved Markov prediction algorithm based on the historical law of demand and the fuzzycomprehensive evaluation algorithm based on expert experience are presented, including thedefinitions and computational procedures related to the demand state and matrix of transitionprobability, the establishment of the influence factor system concerning the demand for thespare parts, the solution of the influence factor weight, etc. On the basis of the above models,the results are combined and optimized.Thirdly, on the basis of the two-level supply guarantee system, the optimal configurationof the spare parts is analyzed by means of simulation and optimization. Through analysis onthe supply efficiency of irreparable parts and repairable parts, the applicable simulationmodels are established as well as the constrained optimization model for the spare partstowards different optimization objectives, based on which the key points of the geneticalgorithm are designed.Finally, the simulation realization of the above research is conducted. The experiment shows that the improved Markov prediction algorithm based on the historical law of demandand the fuzzy comprehensive evaluation algorithm based on expert experience could be wellapplied to the demand prediction of the spare parts and the simulation and optimizationmethod of allocation of the spare parts could satisfactorily indicate the demand for allocationof the spare parts.