民用飞机多级库存配置方法与管理研究
|
摘要
航材的全方位支援能力已经成为影响飞机制造商抢占市场的重要因素。面对新趋势，上海飞机客户服务有限公司在成立早期便面临极大的挑战。在当前形势下，本文以航材为研究对象，以提高航空公司飞机签派可靠性为目的，根据历史需求信息进行需求预测，同时考虑到成本费用、修理级别等因素，从系统的角度来解决实际应用中各种情况下的航材库存配置决策问题，并在解决难点问题的基础上，对航材工程数据进行信息化管理，开发了比较完整的支撑系统。本论文的主要研究内容如下：
(1)对航材需求预测方法进行研究。首先介绍了波音、空客在对航空公司进行初始备件推荐时用到的航材需求量计算模型；接着，对于后续需求预测问题，介绍了常用的连续性及间断性需求方法。针对高价周转件的低需求、随机性、不确定特性，采用模糊逻辑与神经网络相结合的模糊神经网络预测模型，同时采用PSO进化寻优来优化预测模型；最后将模型预测值与其他预测方法，如回归模型方法，以及间断预测方法等，根据预测误差的对比研究证明，该方法有效提高了预测精度。
(2)周转件多级库存优化配置研究。针对周转件供应链上主制造商总部、航空公司基地的航材配置问题，提出了主制造商管理库存模式下面向系统优化的多级库存配置模型。模型在建模中对维修保障供应链等级与维修层级进行抽象及简化。首先，通过分析选取衡量民机效能的关键指标；然后，根据航空公司的运营计划、保障站点组织、航线结构，得到基地的年平均需求量,并根据需求差均比选择合适的分布，描述基地和总部的需求到达过程；接着，为解决民机这种复杂系统的优化效率问题，以机队的保障效能为目标，费用为约束，采用边际分析法进行优化求解；最后给出实例，详细介绍了求解优化过程及结果，并与波音的基于单基地、单航材的数量确定方法进行对比，表明了本文方法的实用性与优越性。
(3)具有横向供应的多级库存优化模型。针对符合(S-1,S)库存策略的周转件多级库存问题，考虑当基地短缺航材时，从邻近基地横向转借，而非从距离更远的主制造商总部申请的情况。首先对随着库存状态变化而改变的基地航材需求率建模。当库存为正的时候，需求等于正常的需求率加上横向供应给其他基地的平均需求率；当库存为零或负值时，此时的需求率等于航材的短缺率；接着建立马尔科夫稳态方程，利用近似算法分别对基地立刻满足的需求概率、通过横向供应满足的需求概率以及短缺的需求概率进行数值求解，利用仿真实验进行验证；最后以航材满足率为约束、库存成本为目标建立库存优化模型，并用启发式算法求解。
(4)以维修BOM为核心对航材支援数据进行集中管理。首先，将来源于设计院、制造厂、供应商、库存与商务管理以及飞机用户等多来源的航材支援所需数据进行梳理分类，从中提取航材属性信息，并基于国际规范和行业标准，建立代码化的航材参数体系；接着，根据修理级别、IPC的可拆卸原则，确定维修BOM的底层结构，以复合式维修BOM取代单树式BOM集中存储航材资料信息；最后，采用模块化的构型管理模式对维修BOM进行有效性控制，并在此基础上提取基于构型的潜在航材集（S集）。通过以上研究为C919大型客机航材的信息化管理和航材计划工作奠定数据基础。
(5)建立了基于数据管理的航材预测与库存配置系统。系统采用浏览器/服务器（B/S）模式组建，是一个覆盖航材基础数据库和航材需求预测、库存优化配置等主要管理功能的综合性软件系统。系统作为航材支援的唯一数据源及管理系统，运行高效，提供的数据全面、可靠,并且具有可扩展性，是C919大型客机航材支援系统的基础性支撑系统之一。
The spare parts supporting capability has become the key factor for the manufactures to seize themarket. In the new situations, the costumer service company faces a great challenge in the initial stage.This thesis focuses on studying spare parts supporting technologies to improve the aircraft dispatchreliability. Based on information management of the spare parts engineering data, the spare partsdemand is forecasted according to the historic demand data. Further considering other factors such asthe cost and the level of repair, the stock allocation decision method in the real situation is developedin a systematic perspective. Based on the research of the thesis, a comprehensive software system isdeveloped. The main contents of the thesis are as the follows:
The aircraft spare part demand forecasting methods are studied. The commonly used continuousand discrete demand forecasting methods are introduced, then in view of the low demand, stochasticand uncertain characteristics of the high price turnover parts, the advanced non-linear signalprocessing method-the wavelet transform (WT)-is used to analyze the spare part demand time seriesdata; Based on the sub-signals, the Fuzzy Neural Network forecasting model, which combine thestrength of the fuzzy logic method and neural network method, is developed and the Particle SwarmOptimization (PSO) method is adopted to optimize the furcating model parameters; Finally the sparepart demand forecasting result are obtained based on the wavelet reconstruction of the prediction ofthe sub-signals. The forecasting errors are analyzed and compared with other forecasting methods,such as the regression method and the discrete forecasting method, which shows the effectiveness ofthe proposed forecasting method.
The optimization method for the multi-echelon inventory allocation of the turnover parts is studied.In view of the allocation of the spare parts of the manufactures and the airliners in the supply chain ofthe turnover parts, the multi-echelon inventory allocation model with the vendor managed inventoryas the main body while considering the systematic optimization. In the model construction the level ofthe maintenance and support supply chain and level of repair are abstracted and simplified. Firstly, thekey indexes for civil aircraft effectiveness evaluation are established, then according to the airlineroperation plan, the structure of the supporting spots, structure of the route, and the average annualspare parts demand of the base is estimated. The arrival process of demands for the depot and baseswas described by the distribution which was chosen by the ratio of variance-to-mean. In view of thecomplexities of aircraft system, with the fleet support as the target and the cost as the constraint, themarginal analysis method is used to resolve the optimization problem. A case study is carried out, presenting a detailed optimizing process and results. The result is compared with the Boeing proposedsingle base and single part method, which shows the effectiveness and advantage of the proposedmethod.
The demand rate at a base depending on the inventory situation is modeled. With positive inventoryon hand the total demand is normal demand plus demand from horizontal transshipments from otherbases. With no positive inventory on hand, the only real demand is the lack rate of the spare parts. TheMarkov equation is adopted to model the problem and the approximation algorithm is used tocompute the instant demand probability, the horizontal transshipments demand probability and thelack probability. With the stock cost as the optimization target and the spare parts demand satisfyingrate as the constraint, the model is established and the heuristic algorithm is used to resolve theproblem.
Firstly, Centered on the maintenance BOM, the spare parts support data is managed in a centralizedstyle. Firstly, the data from designers, the manufactures, the suppliers, the stock and businessmanagers as well as the operators is sorted out, then the air material related information is extractedand the coded air material parameter system is established; according to the level of repair, thedetachable principle of the IPC, the underlying structure of the maintenance BOM is determined andinstead of the tree type BOM, the compound maintenance BOM is adopted to centrally store the airmaterial data; Modularized configuration management method is used to control the effectiveness ofthe maintenance BOM, based on which the configuration–based potential spare parts (S files) areextracted. Through this study the data foundation for air material information management andplanning has been established for C919aircraft.
The data management based spare parts forecasting and stock allocation software system isdeveloped. The B/S architecture is adopted, covering the basic spare parts data, spare parts demandforecasting, stock allocation optimization and so on. The system, as the sole data source for spareparts support and management, can run efficiently, providing comprehensive and reliable data. Thesystem is extendible and is basic one of the C919aircraft spares parts supporting systems
(1)对航材需求预测方法进行研究。首先介绍了波音、空客在对航空公司进行初始备件推荐时用到的航材需求量计算模型；接着，对于后续需求预测问题，介绍了常用的连续性及间断性需求方法。针对高价周转件的低需求、随机性、不确定特性，采用模糊逻辑与神经网络相结合的模糊神经网络预测模型，同时采用PSO进化寻优来优化预测模型；最后将模型预测值与其他预测方法，如回归模型方法，以及间断预测方法等，根据预测误差的对比研究证明，该方法有效提高了预测精度。
(2)周转件多级库存优化配置研究。针对周转件供应链上主制造商总部、航空公司基地的航材配置问题，提出了主制造商管理库存模式下面向系统优化的多级库存配置模型。模型在建模中对维修保障供应链等级与维修层级进行抽象及简化。首先，通过分析选取衡量民机效能的关键指标；然后，根据航空公司的运营计划、保障站点组织、航线结构，得到基地的年平均需求量,并根据需求差均比选择合适的分布，描述基地和总部的需求到达过程；接着，为解决民机这种复杂系统的优化效率问题，以机队的保障效能为目标，费用为约束，采用边际分析法进行优化求解；最后给出实例，详细介绍了求解优化过程及结果，并与波音的基于单基地、单航材的数量确定方法进行对比，表明了本文方法的实用性与优越性。
(3)具有横向供应的多级库存优化模型。针对符合(S-1,S)库存策略的周转件多级库存问题，考虑当基地短缺航材时，从邻近基地横向转借，而非从距离更远的主制造商总部申请的情况。首先对随着库存状态变化而改变的基地航材需求率建模。当库存为正的时候，需求等于正常的需求率加上横向供应给其他基地的平均需求率；当库存为零或负值时，此时的需求率等于航材的短缺率；接着建立马尔科夫稳态方程，利用近似算法分别对基地立刻满足的需求概率、通过横向供应满足的需求概率以及短缺的需求概率进行数值求解，利用仿真实验进行验证；最后以航材满足率为约束、库存成本为目标建立库存优化模型，并用启发式算法求解。
(4)以维修BOM为核心对航材支援数据进行集中管理。首先，将来源于设计院、制造厂、供应商、库存与商务管理以及飞机用户等多来源的航材支援所需数据进行梳理分类，从中提取航材属性信息，并基于国际规范和行业标准，建立代码化的航材参数体系；接着，根据修理级别、IPC的可拆卸原则，确定维修BOM的底层结构，以复合式维修BOM取代单树式BOM集中存储航材资料信息；最后，采用模块化的构型管理模式对维修BOM进行有效性控制，并在此基础上提取基于构型的潜在航材集（S集）。通过以上研究为C919大型客机航材的信息化管理和航材计划工作奠定数据基础。
(5)建立了基于数据管理的航材预测与库存配置系统。系统采用浏览器/服务器（B/S）模式组建，是一个覆盖航材基础数据库和航材需求预测、库存优化配置等主要管理功能的综合性软件系统。系统作为航材支援的唯一数据源及管理系统，运行高效，提供的数据全面、可靠,并且具有可扩展性，是C919大型客机航材支援系统的基础性支撑系统之一。
The spare parts supporting capability has become the key factor for the manufactures to seize themarket. In the new situations, the costumer service company faces a great challenge in the initial stage.This thesis focuses on studying spare parts supporting technologies to improve the aircraft dispatchreliability. Based on information management of the spare parts engineering data, the spare partsdemand is forecasted according to the historic demand data. Further considering other factors such asthe cost and the level of repair, the stock allocation decision method in the real situation is developedin a systematic perspective. Based on the research of the thesis, a comprehensive software system isdeveloped. The main contents of the thesis are as the follows:
The aircraft spare part demand forecasting methods are studied. The commonly used continuousand discrete demand forecasting methods are introduced, then in view of the low demand, stochasticand uncertain characteristics of the high price turnover parts, the advanced non-linear signalprocessing method-the wavelet transform (WT)-is used to analyze the spare part demand time seriesdata; Based on the sub-signals, the Fuzzy Neural Network forecasting model, which combine thestrength of the fuzzy logic method and neural network method, is developed and the Particle SwarmOptimization (PSO) method is adopted to optimize the furcating model parameters; Finally the sparepart demand forecasting result are obtained based on the wavelet reconstruction of the prediction ofthe sub-signals. The forecasting errors are analyzed and compared with other forecasting methods,such as the regression method and the discrete forecasting method, which shows the effectiveness ofthe proposed forecasting method.
The optimization method for the multi-echelon inventory allocation of the turnover parts is studied.In view of the allocation of the spare parts of the manufactures and the airliners in the supply chain ofthe turnover parts, the multi-echelon inventory allocation model with the vendor managed inventoryas the main body while considering the systematic optimization. In the model construction the level ofthe maintenance and support supply chain and level of repair are abstracted and simplified. Firstly, thekey indexes for civil aircraft effectiveness evaluation are established, then according to the airlineroperation plan, the structure of the supporting spots, structure of the route, and the average annualspare parts demand of the base is estimated. The arrival process of demands for the depot and baseswas described by the distribution which was chosen by the ratio of variance-to-mean. In view of thecomplexities of aircraft system, with the fleet support as the target and the cost as the constraint, themarginal analysis method is used to resolve the optimization problem. A case study is carried out, presenting a detailed optimizing process and results. The result is compared with the Boeing proposedsingle base and single part method, which shows the effectiveness and advantage of the proposedmethod.
The demand rate at a base depending on the inventory situation is modeled. With positive inventoryon hand the total demand is normal demand plus demand from horizontal transshipments from otherbases. With no positive inventory on hand, the only real demand is the lack rate of the spare parts. TheMarkov equation is adopted to model the problem and the approximation algorithm is used tocompute the instant demand probability, the horizontal transshipments demand probability and thelack probability. With the stock cost as the optimization target and the spare parts demand satisfyingrate as the constraint, the model is established and the heuristic algorithm is used to resolve theproblem.
Firstly, Centered on the maintenance BOM, the spare parts support data is managed in a centralizedstyle. Firstly, the data from designers, the manufactures, the suppliers, the stock and businessmanagers as well as the operators is sorted out, then the air material related information is extractedand the coded air material parameter system is established; according to the level of repair, thedetachable principle of the IPC, the underlying structure of the maintenance BOM is determined andinstead of the tree type BOM, the compound maintenance BOM is adopted to centrally store the airmaterial data; Modularized configuration management method is used to control the effectiveness ofthe maintenance BOM, based on which the configuration–based potential spare parts (S files) areextracted. Through this study the data foundation for air material information management andplanning has been established for C919aircraft.
The data management based spare parts forecasting and stock allocation software system isdeveloped. The B/S architecture is adopted, covering the basic spare parts data, spare parts demandforecasting, stock allocation optimization and so on. The system, as the sole data source for spareparts support and management, can run efficiently, providing comprehensive and reliable data. Thesystem is extendible and is basic one of the C919aircraft spares parts supporting systems
引文
[1]黄平.航空公司眼中的客户服务与支援上航机务工程部副总经理葛忠汉访谈[J].国际航空,2008,(12):30-32.
[2]成磊.未来10年全球航空维修市场预测[J].航空维修与工程,2012,13(3):26-28.
[3]马援.成为深受客户信赖的终身服务提供商——访波音民用飞机集团中国事务副总裁庄博润和波音中国支援部总监潘迈克[J].国际航空,2008,11(2):23-24.
[4]刘跃.罗克韦尔·柯林斯推出“航材发付”100(Dispatch100)服务解决方案[J].国际航空,2008,(2):24.
[5]波音启动787GoldCare服务[J].航空维修与工程,2010,32(3):15.
[6]空客将向川航提供“空中客车飞行小时服务”[J].空运商务,2010,2(5):24.
[7]同姗姗,常玉.中国大飞机项目客户服务能力的提升研究[J].航空制造技术,2012,11(13):68-71.
[8]汤小平.民机客户服务与标准化[J].航空标准化与质量,2008,(05):4-8.
[9]张力,李卫灵,刘臣宇.航材采购管理系统的设计与实现[J].计算机与现代化,2012,(01):97-99.
[10]张作刚,李卫灵,李丽.基于B/S模式的航材管理信息系统设计与实现[J].计算机与现代化,2011(01):88-90.
[11]苗颖涵,赵盼,张雪胭.直升机航材备件管理信息系统设计[J].价值工程,2011(03):177-178.
[12] Sherbrooke C C. Multi-echelon inventory systems with lateral supply[J]. Naval ResearchLogistics,1992,39(1):29.
[13]程婕.从单打独斗到航材共享[J].科技经济市场,2011,11(3):11-13.
[14]赵淑舫,宁宣熙,吴桐水.航材需求预测模型研究[J].中国民航学院学报,2002,20(3):21-24.
[15]朱新宇吴亮德.航材管理现状及我国的对策[J].科技经济市场,2011(03):83-86.
[16]曾烨.基于泊松理论的航材储备管理[J].科技资讯,2012(05):2.
[17] Willemain T R, Smart C N, Schwarz H F. A new approach to forecasting intermittent demandfor service parts inventories[J]. International Journal of Forecasting,2004,20(3):375-387.
[18]张大鹏,任聪,张戎等.基于时间序列分析的易消耗性备件需求预测研究[J].物流技术,2009,21(5):58-61.
[19]刘文法,王旭,张建邦.基于LS-SVM的装备需求时间序列预测[J].弹箭与制导学报,2006(S4):780-783.
[20]康锐,李瑞莹.基于ARMA模型的故障率预测方法研究[J].系统工程与电子技术,2008,347(08):1588-1591.
[21]刘晓春,黄爱军,马芳等.基于指数平滑技术的装备维修备件需求预测[J].装备环境工程,2012,11(6):109-112.
[22] Croston J D. FORECASTING AND STOCK CONTROL FOR INTERMITTENTDEMANDS[J]. Operational Research Quarterly,1972,23(3):289-303.
[23] Syntetos A A, Boylan J E. On the variance of intermittent demand estimates[J]. InternationalJournal of Production Economics,128(2):546-555.
[24] Syntetos A A, Boylan J E. On the bias of intermittent demand estimates[J]. InternationalJournal of Production Economics,2001,71(1-3):457-466.
[25] Segerstedt A. Inventory control with variation in lead times, especially when demand isintermittent[J]. International Journal of Production Economics,1994,35(1-3):365-372.
[26] Johnston F R, Boylan J E. Forecasting for items with intermittent demand[J]. Journal of theOperational Research Society,1996,47(1):113-121.
[27] Shenstone L, Hyndman R J. Stochastic models underlying Croston's method for intermittentdemand forecasting[J]. Journal of Forecasting,2005,24(6):389-402.
[28] Xu Q, Wang N, Shi H. A Review of Croston's method for intermittent demand forecasting[C].Chongqing, China: IEEE Computer Society,2011.
[29]钟颖,汪秉文.基于遗传算法的BP神经网络时间序列预测模型[J].系统工程与电子技术,2002,22(4):78-92.
[30]荆园园.人工神经网络在客车备件需求预测中的应用研究[D].郑州大学,200
[31]窦云杰,时和平,王上军.基于灰色三角BP神经网络的装备训练需求预测研究[J].装备指挥技术学院学报,2010(06):114-118.
[32]张冬,明新国,赵成雷等.基于BP神经网络和设备特性的工业设备备件需求预测[J].机械设计与研究,2010(01):72-76.
[33]窦云杰,王上军.基于BP神经网络的野外驻训备件需求预测研究[J].兵工自动化,2010(03):33-34.
[34]董蒙,彭绍雄,杨雪.主成分分析—BP神经网络在备件需求预测中的应用[J].物流科技,2010(11):81-84.
[35]康锐,李瑞莹.基于神经网络的故障率预测方法[J].航空学报.2008,No.214(02):357-363.
[36] Ghobbar A A. Forecasting intermittent demand for aircraft spare parts: A comparativeevaluation of methods[J]. Journal of Aircraft,2004,41(3):665-673.
[37] Ghobbar A A, Friend C H. Evaluation of forecasting methods for intermittent parts demand inthe field of aviation: a predictive model[J]. Computers&Operations Research,2003,30(14):2097-2114.
[38] Ghobbar A A, Friend C H. Sources of intermittent demand for aircraft spare parts withinairline operations[J]. Journal of Air Transport Management,2002,8(4):221-231.
[39]杨杰,张斌,华中生.间断需求预测方法综述[J].预测,2005(05):70-75.
[40] Sherbrooke C C. METRIC: Multi-echelon technique for recoverable item control [J].Operations Research,1969,16:122-141.
[41] Demmy W S. ON SHERBROOKE'S METRIC DISTRIBUTION ALGORITHM[J]. Modelingand Simulation, Proceedings of the Annual Pittsburgh Conference,1979.
[42] Burns J F, Sivazlian B D. DYNAMIC ANALYSIS OF MULTI-ECHELON SUPPLYSYSTEMS[J]. Computers and Industrial Engineering,1978,2(4):181-193.
[43] C G S. A multi-echelon inventory model for a repairable item with one-for-onereplenishment[J]. Management Science,1985,31(10):1247-1256.
[44] Sleptchenko A, van der Heijden M C, van Harten A. Effects of finite repair capacity inmulti-echelon, multi-indenture service part supply systems[J]. International Journal ofProduction Economics,2002,79(3):209-230.
[45] Axsater S, Juntti L. Comparison of echelon stock and installation stock policies for two-levelinventory systems[J]. International Journal of Production Economics,1996,45(1-3):303-310.
[46] Axsater S. Modelling Emergency Lateral Transshipments in Inventory Systems[J].Management Scientce,1990,36(11):1329-1338.
[47] Lee H L. A Multi-Echelon Inventory Model for Repairable Items with Emergency LateralTransshipments[Z],1987.
[48] Alfredsson P, Verrijdt J. Modeling emergency supply flexibility in a two-echelon inventorysystem[J]. Management Science,1999,45(10):1416-1431.
[49] Alfredsson P. Optimization of multi-echelon repairable item inventory systems withsimultaneous location of repair facilities[J]. European Journal of Operational Research,1997,99(3):584-595.
[50] Diaz A, Fu M C. Models for multi-echelon repairable item inventory systems with limitedrepair capacity[J]. European Journal of Operational Research,1997,97(3):480-492.
[51] Wong H, Cattrysse D, Van Oudheusden D. Inventory pooling of repairable spare parts withnon-zero lateral transshipment time and delayed lateral transshipments[J]. European Journal ofOperational Research,2005,165(1):207-218.
[52] Wong H, Cattrysse D, Van Oudheusden D. Stocking decisions for repairable spare partspooling in a multi-hub system[J]. International Journal of Production Economics,2005,93-94:309-317.
[53] Wong H, Van Oudheusden D, Cattrysse D. Two-echelon multi-item spare parts systems withemergency supply flexibility and waiting time constraints[J]. Iie Transactions,2007,39(11):1045-1057.
[54] Van Oudheusden Dirk Cattrysse H W A D. Two-echelon multi-item spare parts systems withemergency supply flexibility and waiting time constraints[J]. IIE Transactions,2007,39(11)：1045-1057.
[55] Wong H, Van Oudheusden D, Cattrysse D. Cost allocation in spare parts inventory pooling[J].Transportation Research Part E-Logistics and Transportation Review,2007,43(4):370-386.
[56] Lee L H, Chew E P, Teng S, et al. Multi-objective simulation-based evolutionary algorithm foran aircraft spare parts allocation problem[J]. European Journal of Operational Research,2008,189(2):476-491.
[57] Lee Y H, Jung J W, Jeon Y S, et al. An effective lateral transshipment policy to improveservice level in the supply chain[J]. International Journal of ProductionEconomics,2007,106(1):115-126.
[58]孙江生,李苏剑,吕艳梅等.武器贵重备件三级库存模型仿真研究[J].兵工学报,2008,29(7);266-271.
[59]赵方庚,孙江生,李苏剑.武器装备贵重备件保障模型研究[J].兵工学报,2008,29(5):577-580.
[60]司书宾,贾大鹏,孙树栋等.服务水平约束下的多-单维修备件协同库存控制模型及其仿真研究[J].中国机械工程,2007,18(23):2844-2847.
[61]孙清华,彭志忠.供应链多级库存控制应用研究[J].生产力研究,2008,(2):55-60.
[62]康锐,王乃超.备件需求产生、传播及解析算法研究[J].航空学报,2008,218(05):1163-1167.
[63]王乃超,康锐.基于备件保障概率的多级库存优化模型[J].航空学报,2009,30(6):1043-1047.
[64]李丽,李卫灵史玉敏.抓好标准化建设提升航材管理质量[J].科技信息,2012,(11):245.
[65]殷丽娟.大飞机项目研制中标准化工作作用和地位的思考[J].航空标准化与质量,2010,(5):10-11.
[66]张岩涛,夏晓理.航空产品数据管理标准的应用解析[J].机械工业标准化与质量,2009,(11):44-46.
[67]胡秦赣,骆晶妍.民机构型管理标准化初探[J].航空标准化与质量,2008,(5):12-16.
[68]郑永靖.民用飞机数字化综合维修信息系统的设计与实现[D].华中师范大学,2004.
[69] Maltzahn S, Anderl R. Early BOM derivation from requirement specifications by reusingproduct knowledge[C]. Washington, DC, United states: American Society of MechanicalEngineers.
[70] Lee J H, Kim S H, Lee K. Integration of evolutional BOMs for design of ship outfittingequipment[J]. CAD Computer Aided Design.,44(3):253-273.
[71] Fan Q, Wei Q, Zhang H, et al. Research of product document modeling and BOM multi-viewmapping based on STEP[J]. Journal of Convergence Information Technology.,7(22):550-558.
[72] Fan Q, Zhou R, Wei Q, et al. BOM multi-view mapping based on a single data source underPLM[C]. Xi'an, China: IEEE Computer Society.
[73]魏志强,王先逵,吴丹等.基于单一数据源的产品BOM多视图映射技术[J].清华大学学报(自然科学版),2002,42(6):802-805.
[74]周育洋. PDM中面向信息集成的BOM多视图映射技术研究[D].武汉理工大学,2010.
[75]范玉青,逄淑明,蒋辉.以BOM为主线组织企业CIMS信息流——PDM集成框架中BOM的应用[J].制造业自动化,2001,23(2):17-19.
[76]郑巍,马海晶,曹晶. CIMS系统中BOM表构造方法研究[Z],2006:23(8),142-144.
[77] Wu M, Hsu Y. Design of BOM configuration for reducing spare parts logistic costs[J]. ExpertSystems with Applications,2008,34(4):2417-2423.
[78]司书宾,王辑添.复杂装备维护BOM集成建模方法研究[J].中国制造业信息化,2011(23):50-54.
[79]王辑添,司书宾.复杂装备维护BOM集成建模方法研究[J].中国制造业信息化,2011,40(23):50-54.
[80]董维,张帅,孙树栋.大型复杂装配维护BOM管理系统研究与实践[J].中国制造业信息化,2008,37(9):70-73.
[81]李锋,胡浩,汪崟.适应设备改造的维护BOM表动态生成技术研究[J].制造业自动化,2010,32(11):4-8.
[82]王建民,任艮全,张君.面向信息资源管理的维修BOM结构设计与分析[J].计算机集成制造系统,2010,16(7):1545-1551.
[83]陈飞.基于维修BOM的复杂装备MRO服务管理系统的研究[D].浙江大学,2012.
[84]许承东,裴鑫,宋奕.基于维修过程的军用直升机MRO信息系统功能设计[J].航空维修与工程,2011,(3):35-37.
[85]张欢.面向服务架构的修造企业MRO决策支持系统研究[D].合肥工业大学,2012.
[86]张力,许承东,裴鑫.直升机MRO信息系统需求分析与体系结构[J].计算机集成制造系统,2010,16(10):2285-2292.
[87]王庆林.波音DCAC/MRM系统给我们的启示[J].民用飞机设计与研究,2007,(1):39-44.
[88]卢鹄,于勇,杨五兵等.飞机单一产品数据源集成模型研究[J].航空学报,2010,31(4):836-841.
[89] He L, Ming X, Kong F, et al. Research on airplane BOM management based on single sourceof product data[C]. Paris, France: Trans Tech Publications.
[90]康锐于永利.装备综合保障基础理论及技术的若干问题[J].装甲兵工程学院学报,2010,24(6):1-8.
[91]黄俊.基于可靠性的民机备件支援及其软件系统[D].南京航空航天大学,2006.
[92]许艳萍.基于PDM的民机备件数据管理关键技术研究[D].南京航空航天大学,2008.
[93]徐鹏飞.面向航空公司的备件投资关键技术研究[D].南京航空航天大学,2008.
[94]孙玛丽.基于PDM的民机备件计划关键技术研究[D].南京航空航天大学,2009.
[95]朱芮瑶.民机主制造商备件库存配置优化技术研究[D].南京航空航天大学,2010.
[96]江华莲.模糊环境下基于差分进化算法的备件（Q,r）库存模型研究[D].华中科技大学,2009.
[97] Wong H, van Houtum G J, Cattrysse D, et al. Simple, efficient heuristics for multi-itemmulti-location spare parts systems with lateral transshipments and waiting time constraints[J].Journal of the Operational Research Society,2005,56(12):1419-1430.
[98] Thonemann U W, Brown A O, Hausman W H. Easy quantification of improved spare partsinventory policies[J]. Management Science,2002,48(9):1213-1225.
[99] Rustenburg W D, van Houtum G J, Zijm W H M. Spare parts management for technicalsystems: resupply of spare parts under limited budgets[J]. Iie Transactions,2000,32(10):1013-1026.
[100] Rustenburg J W, van Houtum G J, Zijm W H M. Exact and approximate analysis ofmulti-echelon, multi-indenture spare parts systems with commonality[M],2003:143-176.
[101]范玉青,李向东,檀润华.企业数字化中BOM多视图形态的组织策略[J].机械设计,2005,22(8):12-15.
[102]蒋志超,程宏声,薛蓓依等.轨道交通车辆维修BOM组织模式及应用研究[J]. CAD/CAM与制造业信息化,2012,(10):80-84.
[103]倪现存,左洪福,许娟等.基于PDM的民机航线维修BOM管理系统研究与开发[J].飞机设计,2008,28(3):50-53.
[104]彭英武,王慎,李庆民.串件拼修对策下两级备件维修供应系统动态管理模型[J].航空学报,2009,15(6):112-116.
[105]万晓云. A320飞机维护中串件的应用与实践[J].航空维修与工程.2004(04):67-69.
[106]马保国,童明东,张继强等.串件拼修对航材储存费用的影响[J].中国管理科学,2004,12(12):355-359.
[107]李庆民罗祎阮旻智.多级维修供应下不完全串件系统可用度评估[J].系统工程与电子技术,2012(06):1182-1186.
[108]阮旻智,李庆民,彭英武,等.不完全串件下多层次系统备件方案优化及其可用度评估[J].南京理工大学学报,2012,(05):886-891.
[109]肖蕾,张志峰.基于串件拼修策略的工程装备备件库存控制[J].海军工程大学学报,2012(06):80-83.
[110]阮旻智,李庆民,彭英武等.串件拼修对策下多级维修供应的装备系统可用度评估[J].航空学报,2012(04):658-665.
[111]罗祎,阮旻智,李庆民.多级维修供应下不完全串件系统可用度评估[J].系统工程与电子技术,2012(06):1182-1186.
[112]卫忠,徐晓飞,战德臣,等.协同供应链多级库存控制的多目标优化模型及其求解方法[J].自动化学报,2007(02):67-71.
[113]刘跃伟.基于多目标的供应链网状结构多级库存研究[D].大连海事大学,2009.
[114]李雄伟.串行结构供应链多级库存控制的多目标优化模型[J].中国证券期货.2011(12):152-153.
[115]仲小波. RFID技术在航空维修和航材管理中的应用[D].南京航空航天大学,2011.
[116]何效辉,周斌.基于RFID的数字化仓库管理系统及其在航材库存管理中的应用[J].科技信息,2012(31):117-141.
[2]成磊.未来10年全球航空维修市场预测[J].航空维修与工程,2012,13(3):26-28.
[3]马援.成为深受客户信赖的终身服务提供商——访波音民用飞机集团中国事务副总裁庄博润和波音中国支援部总监潘迈克[J].国际航空,2008,11(2):23-24.
[4]刘跃.罗克韦尔·柯林斯推出“航材发付”100(Dispatch100)服务解决方案[J].国际航空,2008,(2):24.
[5]波音启动787GoldCare服务[J].航空维修与工程,2010,32(3):15.
[6]空客将向川航提供“空中客车飞行小时服务”[J].空运商务,2010,2(5):24.
[7]同姗姗,常玉.中国大飞机项目客户服务能力的提升研究[J].航空制造技术,2012,11(13):68-71.
[8]汤小平.民机客户服务与标准化[J].航空标准化与质量,2008,(05):4-8.
[9]张力,李卫灵,刘臣宇.航材采购管理系统的设计与实现[J].计算机与现代化,2012,(01):97-99.
[10]张作刚,李卫灵,李丽.基于B/S模式的航材管理信息系统设计与实现[J].计算机与现代化,2011(01):88-90.
[11]苗颖涵,赵盼,张雪胭.直升机航材备件管理信息系统设计[J].价值工程,2011(03):177-178.
[12] Sherbrooke C C. Multi-echelon inventory systems with lateral supply[J]. Naval ResearchLogistics,1992,39(1):29.
[13]程婕.从单打独斗到航材共享[J].科技经济市场,2011,11(3):11-13.
[14]赵淑舫,宁宣熙,吴桐水.航材需求预测模型研究[J].中国民航学院学报,2002,20(3):21-24.
[15]朱新宇吴亮德.航材管理现状及我国的对策[J].科技经济市场,2011(03):83-86.
[16]曾烨.基于泊松理论的航材储备管理[J].科技资讯,2012(05):2.
[17] Willemain T R, Smart C N, Schwarz H F. A new approach to forecasting intermittent demandfor service parts inventories[J]. International Journal of Forecasting,2004,20(3):375-387.
[18]张大鹏,任聪,张戎等.基于时间序列分析的易消耗性备件需求预测研究[J].物流技术,2009,21(5):58-61.
[19]刘文法,王旭,张建邦.基于LS-SVM的装备需求时间序列预测[J].弹箭与制导学报,2006(S4):780-783.
[20]康锐,李瑞莹.基于ARMA模型的故障率预测方法研究[J].系统工程与电子技术,2008,347(08):1588-1591.
[21]刘晓春,黄爱军,马芳等.基于指数平滑技术的装备维修备件需求预测[J].装备环境工程,2012,11(6):109-112.
[22] Croston J D. FORECASTING AND STOCK CONTROL FOR INTERMITTENTDEMANDS[J]. Operational Research Quarterly,1972,23(3):289-303.
[23] Syntetos A A, Boylan J E. On the variance of intermittent demand estimates[J]. InternationalJournal of Production Economics,128(2):546-555.
[24] Syntetos A A, Boylan J E. On the bias of intermittent demand estimates[J]. InternationalJournal of Production Economics,2001,71(1-3):457-466.
[25] Segerstedt A. Inventory control with variation in lead times, especially when demand isintermittent[J]. International Journal of Production Economics,1994,35(1-3):365-372.
[26] Johnston F R, Boylan J E. Forecasting for items with intermittent demand[J]. Journal of theOperational Research Society,1996,47(1):113-121.
[27] Shenstone L, Hyndman R J. Stochastic models underlying Croston's method for intermittentdemand forecasting[J]. Journal of Forecasting,2005,24(6):389-402.
[28] Xu Q, Wang N, Shi H. A Review of Croston's method for intermittent demand forecasting[C].Chongqing, China: IEEE Computer Society,2011.
[29]钟颖,汪秉文.基于遗传算法的BP神经网络时间序列预测模型[J].系统工程与电子技术,2002,22(4):78-92.
[30]荆园园.人工神经网络在客车备件需求预测中的应用研究[D].郑州大学,200
[31]窦云杰,时和平,王上军.基于灰色三角BP神经网络的装备训练需求预测研究[J].装备指挥技术学院学报,2010(06):114-118.
[32]张冬,明新国,赵成雷等.基于BP神经网络和设备特性的工业设备备件需求预测[J].机械设计与研究,2010(01):72-76.
[33]窦云杰,王上军.基于BP神经网络的野外驻训备件需求预测研究[J].兵工自动化,2010(03):33-34.
[34]董蒙,彭绍雄,杨雪.主成分分析—BP神经网络在备件需求预测中的应用[J].物流科技,2010(11):81-84.
[35]康锐,李瑞莹.基于神经网络的故障率预测方法[J].航空学报.2008,No.214(02):357-363.
[36] Ghobbar A A. Forecasting intermittent demand for aircraft spare parts: A comparativeevaluation of methods[J]. Journal of Aircraft,2004,41(3):665-673.
[37] Ghobbar A A, Friend C H. Evaluation of forecasting methods for intermittent parts demand inthe field of aviation: a predictive model[J]. Computers&Operations Research,2003,30(14):2097-2114.
[38] Ghobbar A A, Friend C H. Sources of intermittent demand for aircraft spare parts withinairline operations[J]. Journal of Air Transport Management,2002,8(4):221-231.
[39]杨杰,张斌,华中生.间断需求预测方法综述[J].预测,2005(05):70-75.
[40] Sherbrooke C C. METRIC: Multi-echelon technique for recoverable item control [J].Operations Research,1969,16:122-141.
[41] Demmy W S. ON SHERBROOKE'S METRIC DISTRIBUTION ALGORITHM[J]. Modelingand Simulation, Proceedings of the Annual Pittsburgh Conference,1979.
[42] Burns J F, Sivazlian B D. DYNAMIC ANALYSIS OF MULTI-ECHELON SUPPLYSYSTEMS[J]. Computers and Industrial Engineering,1978,2(4):181-193.
[43] C G S. A multi-echelon inventory model for a repairable item with one-for-onereplenishment[J]. Management Science,1985,31(10):1247-1256.
[44] Sleptchenko A, van der Heijden M C, van Harten A. Effects of finite repair capacity inmulti-echelon, multi-indenture service part supply systems[J]. International Journal ofProduction Economics,2002,79(3):209-230.
[45] Axsater S, Juntti L. Comparison of echelon stock and installation stock policies for two-levelinventory systems[J]. International Journal of Production Economics,1996,45(1-3):303-310.
[46] Axsater S. Modelling Emergency Lateral Transshipments in Inventory Systems[J].Management Scientce,1990,36(11):1329-1338.
[47] Lee H L. A Multi-Echelon Inventory Model for Repairable Items with Emergency LateralTransshipments[Z],1987.
[48] Alfredsson P, Verrijdt J. Modeling emergency supply flexibility in a two-echelon inventorysystem[J]. Management Science,1999,45(10):1416-1431.
[49] Alfredsson P. Optimization of multi-echelon repairable item inventory systems withsimultaneous location of repair facilities[J]. European Journal of Operational Research,1997,99(3):584-595.
[50] Diaz A, Fu M C. Models for multi-echelon repairable item inventory systems with limitedrepair capacity[J]. European Journal of Operational Research,1997,97(3):480-492.
[51] Wong H, Cattrysse D, Van Oudheusden D. Inventory pooling of repairable spare parts withnon-zero lateral transshipment time and delayed lateral transshipments[J]. European Journal ofOperational Research,2005,165(1):207-218.
[52] Wong H, Cattrysse D, Van Oudheusden D. Stocking decisions for repairable spare partspooling in a multi-hub system[J]. International Journal of Production Economics,2005,93-94:309-317.
[53] Wong H, Van Oudheusden D, Cattrysse D. Two-echelon multi-item spare parts systems withemergency supply flexibility and waiting time constraints[J]. Iie Transactions,2007,39(11):1045-1057.
[54] Van Oudheusden Dirk Cattrysse H W A D. Two-echelon multi-item spare parts systems withemergency supply flexibility and waiting time constraints[J]. IIE Transactions,2007,39(11)：1045-1057.
[55] Wong H, Van Oudheusden D, Cattrysse D. Cost allocation in spare parts inventory pooling[J].Transportation Research Part E-Logistics and Transportation Review,2007,43(4):370-386.
[56] Lee L H, Chew E P, Teng S, et al. Multi-objective simulation-based evolutionary algorithm foran aircraft spare parts allocation problem[J]. European Journal of Operational Research,2008,189(2):476-491.
[57] Lee Y H, Jung J W, Jeon Y S, et al. An effective lateral transshipment policy to improveservice level in the supply chain[J]. International Journal of ProductionEconomics,2007,106(1):115-126.
[58]孙江生,李苏剑,吕艳梅等.武器贵重备件三级库存模型仿真研究[J].兵工学报,2008,29(7);266-271.
[59]赵方庚,孙江生,李苏剑.武器装备贵重备件保障模型研究[J].兵工学报,2008,29(5):577-580.
[60]司书宾,贾大鹏,孙树栋等.服务水平约束下的多-单维修备件协同库存控制模型及其仿真研究[J].中国机械工程,2007,18(23):2844-2847.
[61]孙清华,彭志忠.供应链多级库存控制应用研究[J].生产力研究,2008,(2):55-60.
[62]康锐,王乃超.备件需求产生、传播及解析算法研究[J].航空学报,2008,218(05):1163-1167.
[63]王乃超,康锐.基于备件保障概率的多级库存优化模型[J].航空学报,2009,30(6):1043-1047.
[64]李丽,李卫灵史玉敏.抓好标准化建设提升航材管理质量[J].科技信息,2012,(11):245.
[65]殷丽娟.大飞机项目研制中标准化工作作用和地位的思考[J].航空标准化与质量,2010,(5):10-11.
[66]张岩涛,夏晓理.航空产品数据管理标准的应用解析[J].机械工业标准化与质量,2009,(11):44-46.
[67]胡秦赣,骆晶妍.民机构型管理标准化初探[J].航空标准化与质量,2008,(5):12-16.
[68]郑永靖.民用飞机数字化综合维修信息系统的设计与实现[D].华中师范大学,2004.
[69] Maltzahn S, Anderl R. Early BOM derivation from requirement specifications by reusingproduct knowledge[C]. Washington, DC, United states: American Society of MechanicalEngineers.
[70] Lee J H, Kim S H, Lee K. Integration of evolutional BOMs for design of ship outfittingequipment[J]. CAD Computer Aided Design.,44(3):253-273.
[71] Fan Q, Wei Q, Zhang H, et al. Research of product document modeling and BOM multi-viewmapping based on STEP[J]. Journal of Convergence Information Technology.,7(22):550-558.
[72] Fan Q, Zhou R, Wei Q, et al. BOM multi-view mapping based on a single data source underPLM[C]. Xi'an, China: IEEE Computer Society.
[73]魏志强,王先逵,吴丹等.基于单一数据源的产品BOM多视图映射技术[J].清华大学学报(自然科学版),2002,42(6):802-805.
[74]周育洋. PDM中面向信息集成的BOM多视图映射技术研究[D].武汉理工大学,2010.
[75]范玉青,逄淑明,蒋辉.以BOM为主线组织企业CIMS信息流——PDM集成框架中BOM的应用[J].制造业自动化,2001,23(2):17-19.
[76]郑巍,马海晶,曹晶. CIMS系统中BOM表构造方法研究[Z],2006:23(8),142-144.
[77] Wu M, Hsu Y. Design of BOM configuration for reducing spare parts logistic costs[J]. ExpertSystems with Applications,2008,34(4):2417-2423.
[78]司书宾,王辑添.复杂装备维护BOM集成建模方法研究[J].中国制造业信息化,2011(23):50-54.
[79]王辑添,司书宾.复杂装备维护BOM集成建模方法研究[J].中国制造业信息化,2011,40(23):50-54.
[80]董维,张帅,孙树栋.大型复杂装配维护BOM管理系统研究与实践[J].中国制造业信息化,2008,37(9):70-73.
[81]李锋,胡浩,汪崟.适应设备改造的维护BOM表动态生成技术研究[J].制造业自动化,2010,32(11):4-8.
[82]王建民,任艮全,张君.面向信息资源管理的维修BOM结构设计与分析[J].计算机集成制造系统,2010,16(7):1545-1551.
[83]陈飞.基于维修BOM的复杂装备MRO服务管理系统的研究[D].浙江大学,2012.
[84]许承东,裴鑫,宋奕.基于维修过程的军用直升机MRO信息系统功能设计[J].航空维修与工程,2011,(3):35-37.
[85]张欢.面向服务架构的修造企业MRO决策支持系统研究[D].合肥工业大学,2012.
[86]张力,许承东,裴鑫.直升机MRO信息系统需求分析与体系结构[J].计算机集成制造系统,2010,16(10):2285-2292.
[87]王庆林.波音DCAC/MRM系统给我们的启示[J].民用飞机设计与研究,2007,(1):39-44.
[88]卢鹄,于勇,杨五兵等.飞机单一产品数据源集成模型研究[J].航空学报,2010,31(4):836-841.
[89] He L, Ming X, Kong F, et al. Research on airplane BOM management based on single sourceof product data[C]. Paris, France: Trans Tech Publications.
[90]康锐于永利.装备综合保障基础理论及技术的若干问题[J].装甲兵工程学院学报,2010,24(6):1-8.
[91]黄俊.基于可靠性的民机备件支援及其软件系统[D].南京航空航天大学,2006.
[92]许艳萍.基于PDM的民机备件数据管理关键技术研究[D].南京航空航天大学,2008.
[93]徐鹏飞.面向航空公司的备件投资关键技术研究[D].南京航空航天大学,2008.
[94]孙玛丽.基于PDM的民机备件计划关键技术研究[D].南京航空航天大学,2009.
[95]朱芮瑶.民机主制造商备件库存配置优化技术研究[D].南京航空航天大学,2010.
[96]江华莲.模糊环境下基于差分进化算法的备件（Q,r）库存模型研究[D].华中科技大学,2009.
[97] Wong H, van Houtum G J, Cattrysse D, et al. Simple, efficient heuristics for multi-itemmulti-location spare parts systems with lateral transshipments and waiting time constraints[J].Journal of the Operational Research Society,2005,56(12):1419-1430.
[98] Thonemann U W, Brown A O, Hausman W H. Easy quantification of improved spare partsinventory policies[J]. Management Science,2002,48(9):1213-1225.
[99] Rustenburg W D, van Houtum G J, Zijm W H M. Spare parts management for technicalsystems: resupply of spare parts under limited budgets[J]. Iie Transactions,2000,32(10):1013-1026.
[100] Rustenburg J W, van Houtum G J, Zijm W H M. Exact and approximate analysis ofmulti-echelon, multi-indenture spare parts systems with commonality[M],2003:143-176.
[101]范玉青,李向东,檀润华.企业数字化中BOM多视图形态的组织策略[J].机械设计,2005,22(8):12-15.
[102]蒋志超,程宏声,薛蓓依等.轨道交通车辆维修BOM组织模式及应用研究[J]. CAD/CAM与制造业信息化,2012,(10):80-84.
[103]倪现存,左洪福,许娟等.基于PDM的民机航线维修BOM管理系统研究与开发[J].飞机设计,2008,28(3):50-53.
[104]彭英武,王慎,李庆民.串件拼修对策下两级备件维修供应系统动态管理模型[J].航空学报,2009,15(6):112-116.
[105]万晓云. A320飞机维护中串件的应用与实践[J].航空维修与工程.2004(04):67-69.
[106]马保国,童明东,张继强等.串件拼修对航材储存费用的影响[J].中国管理科学,2004,12(12):355-359.
[107]李庆民罗祎阮旻智.多级维修供应下不完全串件系统可用度评估[J].系统工程与电子技术,2012(06):1182-1186.
[108]阮旻智,李庆民,彭英武,等.不完全串件下多层次系统备件方案优化及其可用度评估[J].南京理工大学学报,2012,(05):886-891.
[109]肖蕾,张志峰.基于串件拼修策略的工程装备备件库存控制[J].海军工程大学学报,2012(06):80-83.
[110]阮旻智,李庆民,彭英武等.串件拼修对策下多级维修供应的装备系统可用度评估[J].航空学报,2012(04):658-665.
[111]罗祎,阮旻智,李庆民.多级维修供应下不完全串件系统可用度评估[J].系统工程与电子技术,2012(06):1182-1186.
[112]卫忠,徐晓飞,战德臣,等.协同供应链多级库存控制的多目标优化模型及其求解方法[J].自动化学报,2007(02):67-71.
[113]刘跃伟.基于多目标的供应链网状结构多级库存研究[D].大连海事大学,2009.
[114]李雄伟.串行结构供应链多级库存控制的多目标优化模型[J].中国证券期货.2011(12):152-153.
[115]仲小波. RFID技术在航空维修和航材管理中的应用[D].南京航空航天大学,2011.
[116]何效辉,周斌.基于RFID的数字化仓库管理系统及其在航材库存管理中的应用[J].科技信息,2012(31):117-141.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |