本論文考慮於連續監測系統下的維修保養及備品存貨管理之問題，此問題中考慮不同工作站、機台和備品的系統下，同時最佳化狀態基準維護(condition-based maintenance)以及零件備品的存貨管理政策。零件的退化程度模型可以珈瑪過程(gamma process)描述。透過零件上的感應器，我們能夠連續監測零件的退化程度，一旦任一零件退化程度超過預定的閥值時，不完美維修或更換的維修保養動作將被執行。為了找出最佳的備品存貨管理政策及零件退化程度閥值，我們發展一個以模擬為基礎的最佳化方法(KMTR)，此方法以Stochastic Trust-Region Response Surface Method (STRONG) 為基礎，結合克利金近似模型(kriging metamodel)以及Nelder-Mead simplex method進行求解。透過數值研究可以證實本篇提出的模型和方法能達到維修保養成本最小化。

This paper considers condition-based maintenance and spare parts inventory policy simultaneously for a system consisting of different machines and components. The degradation of the components are modeled by Gamma process. By the sensors on the components, we continuously monitor the degradation level of the components, and once the degradation level exceeds the predefined degradation thresholds, imperfect repair maintenance or replacement maintenance are performed. A simulation-based optimization approach is proposed to find the optimal components inventory policy and degradation level thresholds of components. The proposed approach is based on Stochastic Trust-Region Response Surface Method (STRONG), coupled with the Kriging metamodel and the Nelder-Mead simplex method. A numerical study shows that the proposed model and the method can achieve minimized maintenance cost as expected.

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3論文架構 3
第二章 文獻回顧 5
2.1維修保養問題 5
2.2維修保養的模擬最佳化 10
第三章 數學模型 15
3.1符號定義 15
3.2 問題定義 17
3.2.1零件的退化程度 17
3.2.2維修保養政策 18
3.2.3維修保養程度 19
3.2.4維修保養及備品存貨之假設 20
3.3維修保養數學模型 20
第四章 求解方法 23
4.1方法流程及架構 23
4.2定義信賴區域 24
4.3拉丁超立方抽樣(Latin Hypercube Sampling) 25
4.4建立克利金模型 26
4.5找尋最佳解 27
4.6 Ratio-Comparison(RC)及Sufficient-Reduction(SR)測試 31
4.7更新信賴區域 32
第五章 數值實驗 33
5.1測試問題 33
5.1.1測試函數 33
5.1.2參數設定 35
5.1.3數值結果 36
5.2個案研究 39
5.2.1簡單維修保養問題 40
5.2.2複雜維修保養問題 47
第六章 結論與未來研究 58
參考文獻 59

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