一种基于随机森林的备件预测模型研究

对舰船零部件发生故障问题进行故障诊断,并对故障诊断结果进行分析,建立舰船零部件备件需求模型,给出零部件之间的发生故障概率的关系与备件需求特征;将随机森林回归原理应用到了舰船零部件的备件需求预测领域,构建了基于随机森林的预测模型,以及预测结果准确率的评价。用诊断结果数据对算法进行验证,结果表明,将随机森林算法运用到舰船的备件预测领域可以为舰船装备在一次海上任务期内备件配置问题提供参考价值。     

高原驻训航材有寿件需求预测研究

高原驻训航材保障中,有寿件重要度较高,部队必须携带足够的备件数量来满足任务需要.根据高原驻训条件下航材有寿件需求特点,将有寿件的需求分为到寿更换需求和随机故障消耗产生的需求两部分,分别用数学公式和仿真计算方法进行预测,再将两者结果相加为总需求量.通过实例分析验证,采用工程测算方法可以避免逐一分析器材寿命的繁杂,而单独考虑随机故障消耗可以进一步保证有寿件备件不短缺率在90%以上.该预测,该模型可以为航材有寿件到寿情况分析以及有寿件的携行方案制定提供参考.     

基于保障度的战时三级备件保障优化研究

针对装备备件保障度问题,结合现有的研究现状,综合考虑了战时装备保障的实际情况和新旧备件使用寿命的差异,深入分析了备件调度对保障度的影响,以保障度为目标函数,建立了战时三级备件保障模型,运用经典的排序算法对模型进行求解。最后,算例表明该方法是有效性的。     

基地化保障模式下飞机四站装备需求确定研究

基地化保障模式对飞机保障资源需求的影响是制约其发展的重要问题.基于离散事件仿真(DES)方法建立了航空兵场站四站保障过程的数学模型,考虑了四站装备自身故障的影响,并在所建模型的基础上提出运用离散系统仿真软件ARENA对四站保障过程进行仿真.仿真结果表明,与现行的航空兵场站单机种保障策略相比,基地化保障模式能够减少各个机种通用的四站装备数量需求,而单个机种特有的四站装备需求不变.     

基于干扰管理理论的航空备件模型参量扰动性分析

概述了干扰管理理论的内涵、意义以及目标,依据未来不确定因素引起飞机备件需求量变化以及备件供应时限性增强的实际情况,创建了时限一致度算子,优化了备件需求泊松分布模型,提高了备件供应的精确度,并选取了优化后的模型中的多个参量,进行了多因素的扰动性分析,最后构造了算例,验证了本方法的科学性和有效性.     

基于需求分布规律的石化企业备件库存优化研究

石化类企业备件管理的难度较大,主要原因在于其备件需求往往是间断、离散、随机产生.针对石化类企业备件的间断需求特点,提出Markov过程的bootstrap方法取得备件需求的分布规律,在此基础上构建了备件库存优化模型,利用某大型石化企业实际管理运行数据进行了算例及仿真计算,结果验证了备件需求规律统计方法和库存优化模型的优越性,对于该类企业的备件库存管理实践提供了一定的借鉴意义.     

应对突发状况的装备备件供应网络虚拟库存策略研究

研究突发状况引起装备维修备件需求大增时,供应网络中维修站点备件库存策略问题.基于多维修站点的虚拟库构建应对突发状况的备件库存期望成本模型,考虑突发状况的发生概率和突发状况对维修站点的影响概率,比较分析了突发状况发生引起维修需求大增的情况下,维修站点独立应对和构建虚拟库横向联合应对的库存期望成本,通过算例对这两种库存期望成本模型进行了数值分析,验证了构建虚拟库应对突发状况的有效性.     

基于模糊环境的战时多目标物资供应任务规划

战时保障物资的供应对现代战争起到了至关重要的作用,然而物资供应任务却面临诸多不确定性,传统方法难以对此进行科学准确的任务规划.针对该问题,基于可信性理论,建立了模糊环境下的战时多目标保障物资供应任务规划模型,以及相关期望值等价模型.并结合一种先进的智能启发算法-蝙蝠算法对模型进行高效准确的求解,最后,通过一个任务规划应用实例,验证模型及算法的有效性.结果表明设计的模型与算法与实际问题更为接近,符合实际决策需要.     

基于对故障规律统计分析的汽车备件需求预测

汽车备件的需求与汽车故障紧密相关,文章介绍了一种在对汽车故障进行统计分析并确定其分布规律的基础上预测备件需求的方法,预测中需要结合整车保有量的历史数据以及故障与备件的对应表。用统计的方法对某型客车的故障信息进行分析,认为故障的规律可用四种典型的分布进行描述。实例验证了这种方法的准确性高于传统方法,并且在计算机的辅助下可以方便操作。     

面向多阶段任务的武器系统备件优化配置建模

资源优化配置是作战单元维修保障的关键因素.当作战单元执行单阶段任务时,讨论了部件结构为串联系统和k/n系统的任务成功概率建模问题,在此基础上,建立了k/n结构动态变化的多阶段任务系统的任务成功概率模型.在满足系统任务成功概率约束条件下,给出了防空作战单元备件携行量优化模型,并运用边际分析法进行了求解,通过示例表明了模型的正确性.     

基于灰色关联分析与PLS-LSSVM的航材备件需求预测模型

航材备件是保障航空装备日常训练和作战正常使用的重要影响因素,针对部分航材备件样本数据量少,影响因素多且复杂多变,预测结果与装备系统完好性要求偏差较大等问题.建立基于灰色关联分析(GRA)与偏最小二乘(PLS)及最小二乘向量机(LSSVM)相结合的航材备件预测模型,采集某无人机航材备件数据,通过对统计数据进行灰色关联分析,提取航材备件需求的相关因素作为模型训练样本,确定关键因素,利用偏最小二乘对关键因素特征提取,然后将偏最小二乘特征提取后的数据作为最小二乘向量机输入,进行模型构建及分析.通过实验验证了该方法的可行性与适用性,能够满足无人机航材备件预测的实际需要.     

Renewal theory forecasting for stock control

A method is described for forecasting spares demand from data on model sales. The method uses concepts borrowed from renewal theory. The scheme is particularly useful for producing a forecast of all-time future demand for a spare so that a decision may be made about the timing of the final production run.     

Computing Aviation Sparing Policies: Solving a Large Nonlinear Integer Program

Deployed US Navy aircraft carriers must stock a large number of spare parts to support the various types of aircraft embarked on the ship. The sparing policy determines the spares that will be stocked on the ship to keep the embarked aircraft ready to fly. Given a fleet of ten or more aircraft carriers and a cost of approximately 50 million dollars per carrier plus the cost of spares maintained in warehouses in the United States, the sparing problem constitutes a significant portion of the Navy’s resources. The objective of this work is to find a minimum-cost sparing policy that meets the readiness requirements of the embarked aircraft. This is a very large, nonlinear, integer optimization problem. The cost function is piecewise linear and convex while the constraint mapping is highly nonlinear. The distinguishing characteristics of this problem from an optimization viewpoint are that a large number of decision variables are required to be integer and that the nonlinear constraint functions are essentially “black box” functions; that is, they are very difficult (and expensive) to evaluate and their derivatives are not available. Moreover, they are not convex. Integer programming problems with a large number of variables are difficult to solve in general and most successful approaches to solving nonlinear integer problems have involved linear approximation and relaxation techniques that, because of the complexity of the constraint functions, are inappropriate for attacking this problem. We instead employ a pattern search method to each iteration of an interior point-type algorithm to solve the relaxed version of the problem. From the solution found by the pattern search on each interior point iteration, we begin another pattern search on the integer lattice to find a good integer solution. The best integer solution found across all interations is returned as the optimal solution. The pattern searches are distributed across a local area network of non-dedicated, heterogeneous computers in an office environment, thus, drastically reducing the time required to find the solution.     

Optimization models for critical spare parts inventories—a reliability approach

In industries characterized by heavy utilization of equipment and machinery, such as mining, oil & gas, utilities, transportation, adequate stockholding of critical spare parts becomes essential. Insufficient stocks affect overall performance of physical assets, as lack of spares may result in gross penalties, lower availability or increased operational risks. On the other hand, oversized inventories lead to inefficient use of capital and may imply severe expenditures. This paper presents various approaches for the determination of the optimal stock size, when the stock is composed of (i) non-repairable or (ii) repairable parts. The paper is focused on spares for relatively expensive, highly reliable components, rather than on fast-moving spare parts. Optimization criteria considered are minimization of costs, maximization of equipment availability, and the achievement of a desired stock reliability (probability that a spare part request will not be rejected because of the lack of spares in stock). For stock reliability, instantaneous and interval reliability calculations are considered. In addition, models directed to the estimation of the remaining life of a given stock of spare parts (at a certain stock reliability level) are introduced. The paper describes several models subject to practical industrial application, and presents case studies from utilities and mining to illustrate their use.     

智能电表最优更换及备件库存联合决策

智能电表是智能电网运行的关键部件,提高其可靠性和可用度对保证电力的持续不间断供应和准确电能测量至关重要。充足的智能电表库存是其换装与维修的基本保障。本文基于智能电表的故障特性和换装需求分析,建立了智能电表的最优更换与备件库存联合决策模型,并给出了优化方法,以求得可以使系统长期平均运营成本最小的最优更换与备件库存策略。     

A hybrid spares demand forecasting method dedicated to mining industry

The paper addresses the problem of lumpy demand forecasting which is typical for spare parts. Several prediction methods are presented in the paper - traditional techniques based on time series and advanced methods which use artificial neural networks. The paper presents a new hybrid spares demand forecasting method dedicated to mining companies. The method combines information criteria, regression modeling and artificial neural networks. The paper also discusses simulation research related to efficiency assessment of the chosen variable selection methods and its application in the newly developed forecasting method. The assessment of this method is conducted by a comparison with traditional methods and is based on selected forecast errors.     

Forecasting for the ordering and stock-holding of spare parts

A modern military organization like the UK's Royal Air Force is dependent on readily available spare parts for in-service aircraft in order to maximize operational capability. A large proportion of spare parts are known to have an intermittent or slow-moving demand pattern, presenting particular problems as far as forecasting and inventory control are concerned. In this paper, we use extensive demand and replenishment lead-time data to assess the practical value of forecasting models put forward in the literature for addressing these problems. We use an analytical method for classifying the consumable inventory into smooth, irregular, slow-moving and intermittent demand patterns. Recent forecasting developments are compared against more commonly used methods across the identified demand patterns. One recently developed method, a modification to Croston's method referred to as the approximation method, is observed to provide significant reductions in the value of the stock-holdings required to attain a specified service level for all demand patterns.     

A new approach of forecasting intermittent demand for spare parts inventories in the process industries

Accurate demand forecasting is of vital importance in inventory management of spare parts in process industries, while the intermittent nature makes demand forecasting for spare parts especially difficult. With the wide application of information technology in enterprise management, more information and data are now available to improve forecasting accuracy. In this paper, we develop a new approach for forecasting the intermittent demand of spare parts. The described approach provides a mechanism to integrate the demand autocorrelated process and the relationship between explanatory variables and the nonzero demand of spare parts during forecasting occurrences of nonzero demands over lead times. Two types of performance measures for assessing forecast methods are also described. Using data sets of 40 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.