面向维修的装甲装备预防性器材需求预测

Maintenance Oriented Requirement Forecast for Armored Equipment Preventive Materials

将装甲装备器材需求分为预防性器材需求与修复性器材需求,以基层级保障机构年度预防性器材需求为研究对象,考虑保障机构历史年度预防性器材消耗、装备小修计划、维修范围与维修时机等,构建了面向维修的年度预防性器材需求预测模型,设计了基于遗传算法的OHF Elman(Output-Hidden Feedback Elman)神经网络求解算法,并通过算例和对比分析,验证了模型的合理性与适用性。

The requirement of armored equipment material is divided into the requirement for preventive material and repairable material. Taking the annual preventive material requirement of grassroots-level support unit as the research object, maintenance oriented forecasting model for the annual preventive material requirement is built in consideration of the annual preventive material consumption, the equipment minor repair plan, the maintenance scope and the time of maintenance. The algorithm of Output-Hidden Feedback Elman （OHF Elman） neural network based on genetic algorithms is designed to solve the model, and the rationality and applicability of the model are verified by examples and comparative analysis.