基于改进灰色马尔可夫模型的雷达备件需求预测

Demand Prediction of Radar Spare Parts Based on Improved Grey Markov Model

雷达备件是雷达装备维护和修理的重要物质基础,准确预测备件需求对于保持雷达可用性、维持合理备件库存具有重要意义。文章依据较少的备件消耗历史数据,利用改进后的灰色马尔可夫模型对备件需求进行预测,融合灰色模型和马尔可夫模型进行需求预测的优点,减少因样本数据少、随机性大造成的预测偏差,提高模型预测的精准度。最后通过案例验证改进后的模型在雷达备件需求预测中的有效性。

Radar spare parts are important material basis for radar equipment maintenance and repair.Accurate prediction of spare parts demand is of great significance for maintaining radar availability and maintaining reasonable spare parts inventory.Based on less historical data of spare parts consumption,this paper uses the improved grey Markov Model to predict the spare parts demand,and integrates the advantages of grey model and Markov Model for the demand prediction,reduces the prediction deviation caused by the small sample data and large randomness,and improves the precision of model prediction.Finally,the effectiveness of the improved model in radar spare parts demand prediction is verified through a case.