某型导弹引信备件需求预测模型研究

准确的备件需求预测在备件库存管理中尤为重要,但是备件间歇性需求的特点以及需求的历史数据不足使得备件需求预测特别困难,在一些部门,导弹备件库存管理水平决定了设备如何使用和维修.为解决上述问题,建立一种新的预测方法,综合评价备件需求预测的自相关与解释变量的关系:通过回归分析确定由解释变量引起的非零需求,判别一个非零需求发生的原因,进而得到修正后的非零需求序列的预测结果,最后通过LTD估计,得到具体的非零需求预测值.通过理论分析以及实例结果均证明,改进方法在预测精度上优于其它方法,为导弹备件预测研究提供一定的参考与借鉴.     

基于系统的武器备件需求预测研究

在武器装备保障问题的研究中,针对精确化要求和减少浪费问题,需求预测是武器备件保障的重要任务之一,为便于保障决策,将需求预测与库存控制结合起来,提出了一种基于系统的备件需求综合预测方法.对不同类型的备件采用不同的预测方法,依据历史数据选择最佳的预测方案组合,用于下一周期各备件的需求预测.综合预测方法在对备件需求进行预测的同时,还能得出费用约束下的备件库存方案,从而便于保障人员备件进行备件采购决策.进行仿真,结果表明,综合预测方案是合理有效的,为实际需求提供保障.     

基于时间序列的预测模型应用与异常检测

为解决由于产生时间序列数据时的一些不确定因素而导致预测结果在数值上存在较大偏差的问题，通过分析基于时间序列的历史数据，提出基于迭代的异常检测方法，剔除一些可能对时间序列造成影响的历史事件．实验证明该方法可大大提高预测的精度，得到的预测数据对未来的工作和研究有着更好的借鉴作用。     

小波分析和时间序列法相融合的信息安全态势估计

为了提高信息安全态势的估计精度,提出一种小波分析和时间序列分析相融合的信息安全态势估计方法。首先收集信息安全变化的历史数据,然后采用小波分析对数据进行分解,再分别采用时间序列方法对分解后数据进行建模和估计,最后采用小波分析对估计结果进行重构。结果表明,该方法可以准确跟踪信息安全变化趋势,提高信息安全态势估计的精度。     

基于混沌时间序列的城网饱和负荷预测方法

由于饱和负荷预测的时间跨度大、影响因素多,易导致所得结果偏差较大.为此,基于混沌时间序列设计了一种新的城网饱和负荷预测方法.在相空间的基础上,计算负荷时间序列的饱和关联维数,选取重构相空间内的最佳嵌入维数,并定量分析负荷时间序列的混沌特性;然后确定饱和负荷的增长趋势,通过判断指标衡量饱和负荷预测值,并收集城网历史供电信息,获取最大负荷历史数据;运用趋势外推法建立函数模型,利用中值处理法获得模型预测平均值,选取实际负荷时间序列数据 对其精度实施检验,最终完成对城网饱和负荷的预测.实验结果表明:该方法预测结果较为准确,可消耗较短的时间达到较高的预测精度,具有良好的实用价值和应用前景.     

基于深度网络的出租车Pick-up需求预测

在智慧城市建设中,区域的出租车需求预测是一个十分重要的模块。为了预测指定区域未来时刻的出租车需求,本文通过拓展已有序列模型,提出一个多时间分辨率的基于层次注意力机制的循环高速网络（Multi-Time Resolution Hierarchical Attention-Based Recurrent Highway Networks, MTR-HRHN）。MTR-HRHN将对外生数据时空特征的提取和目标数据的时空建模整合到单个框架中,并且通过多分辨率（例如每个小时或者每天）对序列数据不同的时间属性建模,从而捕获更全面的时间模式。最后,在纽约市出租车数据集上评估MTR-HRHN的预测性能。实验结果表明,与其他经典时间序列预测方法相比,MTR-HRHN在多个高需求区域的短期需求预测上表现出更好的预测性能。     

基于核方法的非线性时间序列预测建模

提出了一种基于核的非线性时间序列预测建模方法。对非线性时间序列的相空间进行重构以确定其嵌入维数，并提出一种基于核主成分分析的非线性时间序列相空间重构方法，针对时间序列的时序特征，采用一种加权的支持向量回归模型对时间序列预测建模。在不同基准数据集上的实验结果表明，与通常的基于普通支持向量回归的建模方法相比，该文所提出的预测建模方法具有较高的精度，说明所提方法对非线性时间序列的预测建模是有效的。     

基于DP的备件库存控制模型研究

备件库存控制是制造企业备件管理中的重要问题.本文针对备件需求的高度不确定性特点,在备件需求预测的基础上,提出一种基于动态规划(DP)的备件库存控制模型以确定备件库存的采购计划.并应用实例来证明本方法的可行性与有效性.     

改进BP网络在航材需求预测中的应用

针对航材备件需求预测问题,在对影响航材备件需求量的多个因素进行分析研究的基础上,运用改进BP神经网络算法进行预测的仿真实验。实验结果表明,改进BP神经网络能够对积累的历史数据进行充分的应用,并且有较高的预测准确性。     

蚁群优化支持向量机的物流需求预测

为了提高物流需求预测精度,针对物流需求的复杂变化特性,提出一种蚁群算法ACO)优化最小二乘支持向量机的(LSSVM)的物流需求预测模型(ACO-LSSVM).首先对物流需求数据进行重构,然后采用LSSVMY刻画物流需求的复杂非线性变化特性,并通过ACO算法优化选择LSSVM参数,采用物流需求预测实例对ACO-LSSVM性能进行测试.结果表明,ACO-LSSVM提高了物流需求预测精度,是一种有效的物流需求预测方法.     

RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting

The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car sales demand are often recurrent. The renowned empirical method adopts historical demand data to create the distribution of lead time demand. Although it works reasonably well when service requirements are relatively low, it has difficulty reaching high target service levels. In this paper, we proposed Recurrent Neural Networks/ Long-Short Term Memory (RNN / LSTM) with modified Adam optimizer to predict the demand for spare parts. In this LSTM, weight vectors are generated respectively. These weights are optimized using the Modified-Adam algorithm. The accuracy of the forecast and the performance of the inventory are considered in the experimental result. Experimental results confirm that RNN / LSTM with a Modified-Adam works well with minimal error compared to other existing methods. We conclude that the proposed RNN/LSTM with Modified-Adam algorithm is well suited for the prediction of automobile spare parts.

     

面向任务的航空备件需求仿真

航空备件是航空装备维修保障合理的物质基础,装备维修精确化要求对备件需求进行合理预测,而任务是其适时、适量的主要参照对象。以备件需求预测为研究对象,以任务为中心,针对备件寿命分布复杂的特点,综合考虑装备使用可用度和经济性约束,利用蒙特卡洛建立备件需求仿真模型,采用边际效益分析优化求解,并用实例验证了模型的可行性和有效性。     

基于工程应用的备件分析及仿真研究

为解决型号研制中备件分析流程指导性不强、备件预测计算模型选控对比原则缺失、备件配置大量冗余的现状,从装备使用维护任务出发,开展基于工程应用的备件分析方法及仿真研究.依据行业标准规范,结合型号备件配置经验,制定基于工程应用的备件分析流程.以最少的保障资源需求满足装备固有的可靠性和安全性水平为前提,开展预防性维修备件分析,建立航空装备状态与保障资源的映射.选取适用于工程应用的修复性维修备件预测模型,基于Matlab仿真完成各预测模型选控对比研究.通过开展基于工程应用的备件分析及仿真研究,可实现航空装备备件的定性分析和定量计算,减少备件冗余,提高装备可用性和备件资源利用率.     

基于可靠度的导弹维修备件需求评估方法研究

通过研究装备维修过程中器件的固有可靠性和维修性,利用系统可靠性分析方法,建立相应的需求数学模型,最后给出了维修备件需求的评估方法。该方法真实反映了导弹维修备件需求规律,且可以推广到其他类似装备备件的需求使用。     

基于案例的备件需求预测技术及软件

运用基于案例的推理（CBR）原理，提出一种备件需求的预测技术，研究了相似识别、相似度，相似基准系统等问题。并设计、并发基于案例推理的备件需求预测软件系统。     

Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline fleets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used, in order to compare different forecasting methods when facing intermittent demand. The results confirm the continued superiority of the weighted moving average, Holt and Croston method for intermittent demand, whereas most commonly used methods by airlines are found to be questionable, consistently producing poor forecasting performance. We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. A simple example is presented to illustrate the performance of the mathematical model. It is suggested that these findings may be applicable to other industrial sectors, which have similar demand patterns to those of airlines.Scope and purposeDemand forecasting is one of the most crucial issues of inventory management. Forecasts, which form the basis for the planning of inventory levels, are probably the biggest challenge in the repair and overhaul industry, as the one common problem facing airlines throughout the world is the need to know the short-term part demand forecast with the highest possible degree of accuracy. The high cost of modern aircraft and the expense of such repairable spares as aircraft engines and avionics constitute a large part of the total investment of many airline operators. These parts, though low in demand, are critical to operations and their unavailability can lead to excessive down time costs. Most airline materials managers deal with intermittent demand, which tends to be random and has a large proportion of zero values. In an effort to achieve this, the study has presented a model that could be of great benefit to airline operators and other maintenance service organisations. It will enable them to select in advance the appropriate forecasting method that better meets their cyclical demand for parts. This approach is consistent with the purpose of this study, which aims to compare different forecasting methods when faced with intermittent demand.     

Spare parts inventory control considering stochastic growth of an installed base

Installed base is a measure describing the number of units of a particular system actually in use. To maintain the performance of the installed units, spare parts inventory control is extremely important and becomes very challenging when the installed base changes over time. This problem is often encountered when a manufacturer starts to deliver a new product to customers and agrees to provide spare parts to replace failed units in the future. To cope with the resulting non-stationary stochastic maintenance demand, a spare parts control strategy needs to be carefully developed. The goal is to ensure that timely replacements can be provided to customers while minimizing the overall cost for spare parts inventory control. This paper provides a model for the aggregate maintenance demand generated by a product whose installed base grows according to a homogeneous Poisson process. Under a special case where the product’s failure time follows the exponential distribution, the closed form solutions for the mean and variance of the aggregate maintenance demand are obtained. Based on the model, a dynamic (Q, r) restocking policy is formulated and solved using a multi-resolution approach. Two numerical examples are provided to demonstrate the application of the proposed method in controlling spare parts inventory under a service level constraint. Simulation is utilized to explore the effectiveness of the multi-resolution approach.     

基于序列运算理论的维修备件保障决策模型

备件保障过程存在大量不确定性, 使其决策困难. 本文以序列运算理论的概率性序列为基础, 把需求、维修、存储的备件数量看作一维离散的随机变量, 通过随机序列之间的卷和、卷差和交积运算来分析保障过程中各随机事件之间的相互作用, 从而对保障过程进行动态描述; 通过概率性序列的期望值理论确立了备件需求满足率来预测库存备件补充的时机和数量, 从而为备件存储决策提供依据.     

间歇性时间序列的可预测性评估及联合预测方法

在高端制造企业的运维业务中,配件需求随机发生,且伴随有大量的零需求阶段,同时,对应的配件需求数据量小,且呈现出间歇性和块状分布的特点,导致现有时间序列预测方法难以有效预测配件需求走势。为解决该问题,提出了一种间歇性时间序列的可预测性评估及联合预测方法。首先,提出了一种新的间歇相似度指标,通过统计两条序列中“0”元素出现的频次和位置,并结合最大信息系数和平均需求间隔等度量指标,有效评估了序列的趋势信息和波动规律,并实现了对间歇性序列可预测性的量化;其次,基于该指标,构建了一个间歇相似度层次聚类方法来自适应地筛选相似性高、可预测性强的序列,剔除极度稀疏、无法预测的序列;此外,探索利用序列间的结构化信息,并构建多输出支持向量回归（M-SVR）模型,从而实现小样本下的间歇性序列联合预测;最后,分别在两个公开数据集（UCI礼品零售数据集和华为电脑配件数据集）和某大型制造企业实际配件售后数据集上进行实验。实验结果表明,相比多个典型的时间序列预测方法,所提方法可有效挖掘各类间歇性序列的可预测性,提高小样本间歇性序列的预测精度,从而为制造企业配件需求预测提供了一种新的解决方案。     

基于数据挖掘的复杂装备维修辅助决策研究

文章通过某装备维修信息框架分析,维修数据预处理,然后以维修记录为例,挖掘出了维修件之间关联规则,并采用决策树分类方法对维修件进行了分类,这些规则和分类结果可以为维修计划制定、器材采购、视情维修等方面提供决策依据。