跳到主要內容

臺灣博碩士論文加值系統

(100.28.0.143) 您好!臺灣時間:2024/07/19 16:10
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:賴俊樺
研究生(外文):Lai, Jun-Hua
論文名稱:改善軍用飛機壽期階段之備份件預測-以A公司為例
論文名稱(外文):Improving Spares Forecasting for Military Aircraft Lifecycle:A Case Study of A Company
指導教授:吳建瑋吳建瑋引用關係
指導教授(外文):WU, CHIEN-WEI
口試委員:林東盈劉時玟王姿惠
口試委員(外文):Lin, Dung-YingLiu, Shih-WenWang, Zih-Huei
口試日期:2023-12-22
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系碩士在職專班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:112
語文別:中文
論文頁數:64
中文關鍵詞:初次備份件平均飛行需求間隔時間時間序列預測方法間歇型波浪型
外文關鍵詞:Initial SparesMean Flight Time Between DemandTime-series Forecasting MethodsIntermittentLumpy
相關次數:
  • 被引用被引用:0
  • 點閱點閱:20
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
軍用飛機可修件在壽期各階段執行的預測作為,如獲得初期所規劃的初次備份件數量,與機隊運作後預測每年故障需求,兩者之準確度極顯重要;一旦預測失準,輕則增加國防預算支出,重則影響飛機妥善與機隊維持。過往估算可修件初次備份件時,所有品項皆套用相同參數,容易造成預測失準衝擊預算編列;而年度故障需求預測只考量近三年平均值,易造成「備而未耗、耗而未備」擴大預算缺口。
本研究分別針對備份件在壽期不同階段需求,深入研析可修件的失效與實際需求資料,探討它們之間的關聯性和差異性。在獲得階段經由參數精進,以實際機隊運作時的平均飛行需求間隔時間(Mean Flight Time Between Demand, MFTBD)取代傳統只計算非預期失效的平均飛行失效間隔時間(Mean Flight Time Between Failures, MFTBF),有效改善初次備份件建議數量,在建案初期預算限制下,仍能滿足機隊運作所需;同時當機隊進入維持階段,可修件故障頻率相對不穩定,也藉由整合多種可處理時間序列資料的預測方法,提升故障型態(如間歇型、波浪型)的年度需求預測準確度。
這項研究的成果確實能夠有效協助A公司後勤單位,在推展類似案例的過程時,提供更深入與科學化的實用資訊來協助機隊穩健的維持,對於提高公司運營效率和客戶滿意度具有重要參考價值。
The accuracy of spare part predictions at various stages of a military aircraft’s lifecycle is crucial, such as the initial spares estimated during the acquisition phase and the annual demand prediction for repairable items during the maintenance phase. Inaccuracies in these predictions can lead to either a minor increase in defense budget expenditures or significant disruptions in the proper maintenance and operation of the aircraft fleet.
Previously, predicting the demand for initial spares involved applying identical parameters to all items, resulting in inaccurate predictions that had implications for budget allocation. Furthermore, the prediction of the annual demand for repairable items only considered the recent 3-year average, potentially leading to an excess or shortage of spares and widening the budget gap.
This study aims to analyze failure data alongside actual demand, exploring the correlation and distinctions between the two. The primary objectives include refining estimation parameters, particularly during the Acquisition Phase, where Mean Flight Time Between Demand (MFTBD) is proposed as a replacement for Mean Flight Time Between Failures (MFTBF) for initial spares, thereby improving quantity predictions. Simultaneously, the study employs Time-Series forecasting methods to establish prediction models for various failure patterns, enhancing the accuracy of predicting annual demand for repairable items, especially for relatively unstable types of failure patterns such as Intermittent and Lumpy ones.
The outcomes of this study serve to empower A Company’s logistics unit in providing customers with more in-depth and scientifically grounded practical information in similar cases. This assistance contributes to the stable maintenance of the fleet, offering significant reference value for improving the operational efficiency and customer satisfaction of the company.
摘要 I
ABSTRACT II
誌謝 IV
目錄 V
圖目錄 VII
表目錄 VIII
第一章 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究架構 4
第二章 文獻探討 6
2-1 整體後勤支援規劃 6
2-1-1 維修規劃 6
2-1-2 獲得階段工作要項 6
2-1-3 後勤支援資料庫 7
2-1-4 SMR代碼重要性 8
2-2 補給支援 10
2-2-1 主要目的 10
2-2-2 預期目標 10
2-3 初次備份件預測 10
2-4 智慧演算法 12
第三章 研究方法 15
3-1 初次備份件精進 16
3-1-1 以需求率取代失效率 19
3-1-2 建立參考機型系統別數據庫 21
3-1-3 方法驗證 21
3-2 可修件年度故障需求預測精進 22
3-2-1 故障需求型態 22
3-2-2 運用智慧演算法 23
3-2-3 演算法介紹 24
3-2-4 模型評估指標 26
3-2-5 故障需求型態推薦模型 27
第四章 實務驗證與分析 28
4-1 初次備份件預測 28
4-1-1 資料研析 28
4-1-2 個案試驗 32
4-1-3 驗證結果 36
4-2 可修件年度故障需求預測 38
4-2-1 預測結果評價 38
4-2-2 故障需求型態預測模型推薦 46
第五章 結論 47
5-1 結論與管理意涵 47
5-2 研究限制與建議 48
參考文獻 49
附錄一、平穩型實驗數據 53
附錄二、間歇型實驗數據 56
附錄三、規律不穩定型實驗數據 59
附錄四、波浪型實驗數據 62
英文文獻
Adur Kannan, B., Kodi, G., Padilla, O., Gray, D., & Smith, B. C. (2020). Forecasting spare parts sporadic demand using traditional methods and machine learning-a comparative study. SMU Data Science Review, 3(2), 9.
AFMC. (1995). Functions and responsibilities of the equipment specialist during provisioning. Air Force Materiel Command, Instruction 23-104.
AFMC. (2019). Maintenance data documentatio. Air Force Materiel Command, Manual 00-20-2.
AFMC. (2023). Provisioning. Air Force Materiel Command, Manual 20-106.
AFSC. (2021). Af technical order system source, maintenance, and recoverability coding of air force weapons, systems, and equipments. Air Force Sustainment Center, Manual 00-25-195.
Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5-6), 594-621.
Bartlett, P. L., Long, P. M., Lugosi, G., & Tsigler, A. (2020). Benign overfitting in linear  regression. Proceedings of the National Academy of Sciences, 117(48), 30063-30070.
Colson, J. (2014). Product Support Analysis, Logistics Product Data, & Reliability, Availability and Maintainability. RAM 7 Training Summit, Logistics Support Activity(LOGSA).
Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289-303.
DAU. (2021). Integrated Product Support Element Guidebook. Defense Acquisition University, 128-129.
Department of Defense. (2015). Department of defense handbook: level of repair analysis, MIL-HDBK-1390. Washington DC: U.S. Department of Defense.
Department of Defense. (2018). Manuals, Technical - Work Unit Code, MIL-DTL-38769G. Washington DC: U.S. Department of Defense.
Esling, P., & Agon, C. (2012). Time-series data mining. ACM Computing Surveys (CSUR), 45(1), 1-34.
Ghobbar, A. A., & Friend, C. H. (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Computers & operations research, 30(14), 2097-2114.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at forecast-accuracy metrics for intermittent demand. Foresight: The International Journal of Applied Forecasting, 4(4), 43-46.
Huiskonen, J. (2001). Maintenance spare parts logistics: Special characteristics and strategic choices. International Journal of Production Economics, 71(1):125-133.
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey.  Philosophical Transactions of the Royal Society A, 379(2194)
Liu, H., Zhang, Y., Ma, Y., Wu, J., Liu, Z. & Meng, L. (2018). Research on Development of Spare Parts. Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 2739-2743.
Mohammadipour, M. (2013). Intermittent demand forecasting with integer autoregressive moving average models.Brunel University London.
NAVAIR. (2004). Design Interface Maintenance Planning Guide. Naval Air Systems Command, 00-25-406.
NAVAIR. (2005). Guidelines for the naval aviation reliability-centered maintenance process. Naval Air Systems Command, 00-25-403.
Petropoulos, F., Kourentzes, N., & Nikolopoulos, K. (2016). Another look at estimators for intermittent demand. International Journal of Production Economics, 181, 154-161.
Romeijnders, W., Teunter, R., & Van Jaarsveld, W. (2012). A two-step method for forecasting spare parts demand using information on component repairs. European Journal of Operational Research, 220(2), 386-393.
SAE International. (2013). Product Support Analysis, TA- STD-0017. U.S.
Shale, E. A., Boylan, J. E., & Johnston, F. R. (2006). Forecasting for intermittent demand: the estimation of an unbiased average. Journal of the Operational Research Society, 57(5), 588-592.
Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International journal of production economics, 71(1-3), 457-466.
Syntetos, A. A., & Boylan, J. E. (2010). On the variance of intermittent demand estimates. International Journal of Production Economics, 128(2), 546-555.
Syntetos, A. A., Boylan, J. E., & Croston, J. D. (2005). On the categorization of demand patterns. Journal of the Operational Research Society, 56:5, 495-503.
Syntetos, A. A., & Boylan, J.E. (2006). On the stock control performance of intermittent demand estimators. International Journal of Production Economics, 103(1), 36-47.
Vasumathi, B., & Saradha, A. (2013). Forecasting Intermittent Demand for Spare Parts. International Journal of Computer Applications, 75(11).
Willemain, T. R., Smart, C. N., Shockor, J. H., & DeSautels, P. A. (1994). Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method. International Journal of forecasting, 10(4), 529-538.
Xiao, B., Lv, L., & Wang, T. (2013). Notice of Retraction: Auxiliary decision support system designing of aircraft's initial spares. In 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE) (pp. 1957-1961). IEEE.
Yang, Y., Ding, C., Lee, S., Yu, L., & Ma, F. (2021). A modified Teunter-Syntetos-Babai method for intermittent demand forecasting. Journal of Management Science and Engineering, 6(1), 53-63.
中文文獻
王俊傑. (2019),以隨機森林演算法預測電信設備之平均故障間隔,國立高雄科技大學電子工程系碩士論文。
余奇潔. (2013),美元兌新台幣匯率預測 -ARIMA與SVM之應用-,國立高雄應用科技大學金融資訊研究所碩士論文。
范淼. (1998),後勤管理導論。臺北:黎明文化出版社。
曹琇茹. (2018),運用 ARIMA 模型於汽車零件之需求預測與分析,明志科技大學工業工程與管理系碩士班碩士論文。
陳孟吟. (2014),醫療衛材之間歇性需求消耗型態與預測模式之研究,東海大學工業工程與經營資訊學系碩士論文。
陳愷謙. (2018),最終訂單問題的備料需求預測,國立清華大學統計學研究所碩士論文。
陳詠進. (2006),多種需求分配下備份件補給系統的存貨配置,國立交通大學工業工程與管理系碩士論文。
陳偉彬. (2005),從可用度觀點探討維修政策與備份件之預估,國防管理學院後勤管理研究所碩士論文。
黃崇豪. (2002),少量多樣性零附件全壽期備料模式之研究,國防管理學院後勤管理研究所碩士論文。
黃種明. (2004),禁忌搜尋法在維修政策與備份件預估之探討,國防管理學院後勤管理研究所碩士論文。
楊邦溢. (2013),應用RCM與TPM協同規劃維修策略之績效評估,國立勤益科技大學工業工程與管理系碩士班碩士論文碩士論文。
網站文獻
1. AWS Machine Learning (2022)。〈什麼是機器學習〉.
https://aws.amazon.com/tw/what-is/machine-learning/
2. 漢翔航空工業 (2021)。〈2021年永續報告書〉.
https://www.aidc.com.tw/Content/File/sr202100ch.pdf
電子全文 電子全文(網際網路公開日期:20290111)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊