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.

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