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In view of the problem of two-Echelon stock between Depot and bases by the system methods, put forward to use the least shortage as the objective function, the total prices not greater than the total support cost as the constraint condition, established the two-Echelon stock optimization model of the lifeless Recoverable parts. Solved it with the marginal analysis method, analyzed the application...
For the problem that the demand of vari-indenture Recoverable Parts did not submit to the Poisson distribution, put forward the Negative binomial distribution to improve the forecasting accuracy. Used the fill rate to estimate the supply degree of Recoverable Parts, the restricted total security funds and the lowest fill rate as the constraint conditions, searching for the fill rate maximization as...
In view of the characteristics that the linear exponential smoothing, secondary exponential smoothing, cubic exponential smoothing had different fitting degree when predicted the spares with the different consumption discipline, optimized results of these three methods through the combination prediction model, and solved it by genetic algorithm and used the obtained results with minimum error as spares...
Design the algorithm of BP neural network applied to the spares consumption prediction, according to the annual flight plan, ascertain the input layer neurons of network, put forward the methods to determine the number of hidden layer neurons, as well as initial weights and deviation to cause the network with good generalization and make certain to get the optimal solution. Through practical examples...
Spares have many kinds and complex specifications, its prediction is difficult, for the problem, the paper proposes the use of nonlinear characteristics of BP neural networks and self-learning ability, based on historical data of spares consumption trains the network of all spares to determine its network model, and used for the future consumption forecast for next year. Through the predictive value...
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