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.     

基于支持向量机的备件需求预测研究

针对传统以统计学为基础的预测方法难以解决小样本预测精度不高的实际问题,将支持向量机回归原理应用到备件需求预测领域,构建基于支持向机备件需求预测模型,以及需求预测结果准确率的评价指标。以实际数据为例,分别运用了指数平滑法、网格搜索法优化参数的支持向量机和遗传算法优化参数的支持向量机进对重点备件的需求量进行预测,验证了遗传算法优化的支持向量机预测性能的先进性。结果证明将支持向量机理论应用到备件保障领域具有重要的实用价值。     

一种结合IPSO-BP神经网络的备件供应模型研究

备件供应是制造业服务价值链协同中的重要组成,也是企业制定销售计划和生产计划的重要依据.本文将备件供应过程中的备件消耗考虑在内,以最小化总成本为目标,以订货时的备件需求量为核心参数,提出一种基于神经网络的备件供应需求预测模型.在现有标准粒子群算法的基础上,通过将惯性权重的改进、环境检测策略和自适应最优解跳跃策略结合,提出...     

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。     

基于ASP技术的备件管理信息系统的开发

介绍了计算机技术和网络技术在备件管理中的应用，运用ASP在基于Web和数据库系统设计上的应用技术，开发备件管理信息系统。     

基于B/S的钢铁企业备件仓库管理系统的设计与开发

从系统规划、分析、设计三个阶段，详细论述了基于B／S模式的备件仓库管理信息系统的设计、开发过程。对钢铁企业规范备件仓库管理信息系统开发具有一定的参考价值。     

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.     

武器装备战时备件保障能力评估

基于备件保障在武器装备作战中的重要地位，提出了战时备件保障的评估指标，建立了战时备件评估的数学模型并介绍了模型的运用情况。     

基于回归分析的备件故障率预测模型

根据备件的故障率可以有效地指导备件的存储策略，该文在介绍了几种典型故障率曲线的基础上，给出了一般预测模型的步骤及其流程图，此后建立了一个基于回归分析的备件故障率预测模型，并利用此模型对某一备件的故障率进行了预测，最后，利用相关系数对二者的相关性进行了分析，说明了二者有较好的线性关系。     

分布式环境下基于语义相似的案例检索

分布式环境下的异构案例表达制约了案例检索过程中案例属性之间的可比性，进而成为分布式环境下案例推理系统成败的一个关键问题。该文提出基于语义相似的案例检索，通过利用Ontology技术来理解案例属性的内在含义，在此基础上定义并计算属性之间的相似程度。对原型系统的初步测试证明了基于语义相似的案例检索有效性。     

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。     

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.

     

基于影响因素分析和数据重构的备件需求预测

针对备件需求具有间断性需求特点,在实践中预测值与真实值往往具有很大偏差的问题,指出历史数据混淆和需求产生原因不明确是造成偏差的两项根本原因。为此,提出了基于影响因素分析和数据重构的备件需求预测方法。首先,在历史数据重构处理中,通过数量退化和时间序列变换,将间断性的需求序列转换为需求间隔的连续性时间序列。其次,在影响因素识别方面,结合实践调研,从备件自身、设备使用、操作人员及突发事故四个方面提出备件需求的七个影响因素,并通过灰色关联分析进行因素筛选。最后,利用SVR预测模型完成备件需求预测,并通过实例企业的数据验证证明了整套方法的可行性与有效性。     

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。     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

Gaussian case-based reasoning for business failure prediction with empirical data in China

Case-based reasoning (CBR) is an easily understandable concept. Business failure prediction (BFP) is a valuable tool that can assist businesses take appropriate action when faced with the knowledge of the possibility of business failure. This study aims to improve the performance of a CBR system for BFP in terms of accuracy and reliability by constructing a new similarity measure, an area seldom researched in the domain of BFP. In order to fulfill this objective, we present a hybrid Gaussian CBR (GCBR) system and use it in BFP with empirical data in China. The heart of GCBR is similarity measure using Gaussian indicators. Feature distances between a pair of cases on each feature are transferred to Gaussian indicators by Gaussian transformations. A combiner is used to generate case similarity on the basis of the Gaussian indicators. Consensus of nearest neighbors is used to generate forecasting on the basis of case similarity. The new hybrid CBR system was empirically tested with data collected from the Shanghai Stock Exchange and Shenzhen Stock Exchange in China. We statistically validated our results by comparing them with multiple discriminant analysis, logistic regression, and two classical CBR systems. The results indicated that GCBR produces superior performance in short-term BFP of Chinese listed companies in terms of both predictive accuracy and coefficient of variation.     

Case‐based curve behaviour prediction

Case‐based reasoning (CBR) is the area of artificial intelligence where problems are solved by adapting solutions that worked for similar problems from the past. This technique can be applied in different domains and with different problem representations. In this paper, a system curve base generator (CuBaGe) is presented. This framework is designed to be a domain‐independent prediction system for the analysis and prediction of curves and time‐series trends, based on the CBR technology. CuBaGe employs a novel curve representation method based on splines and a corresponding similarity function based on definite integrals. This combination of curve representation and similarity measure showed excellent results with sparse and non‐equidistant time series, which is demonstrated through a set of experiments. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

基于卷积的时态CBR快速检索算法及应用*

探讨了如何增强CBR对一种常见的时态信息,即时间序列数据的检索能力;分析了已有的基于傅里叶频谱分析的时间序列检索算法应用于CBR时遇到的问题,并根据时态CBR检索的需要,提出了一种新的基于循环卷积和傅里叶变换时间序列检索算法.理论分析和数值实验结果都证明,提出的算法在检索效率上有一定的优势.将采取这种检索方法的时态CBR应用于时间序列的预测问题中,取得了较好的预测效果且具有较高的预测效率.