在不同領域中對於模型的建置因考量特性多寡而存在不同細緻度的模型。然而不同細緻度模型各有其優缺點，當細緻度越高時進行系統評估時雖然能夠較準確，卻會需要耗費較長的建置時間及成本；而低細緻度模型雖可能存在變異但其績效趨勢可能會反映部份高細緻度模型之績效趨勢。因此本研究引用MO2TOS(Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling)架構並以彈性製造系統(Flexible Manufacturing System)的同步進行機台與車輛排程為對象，透過考量區域控制以及替代機台的特性，設置本研究之高、低細緻度模型。
在MO2TOS架構中，如何依據低細緻度模型績效進行分組將會是運用MO2TOS進行後續抽樣之重點。由於MO2TOS採用等距方式進行方案分組，然而高細緻度模型績效中相似解之個數並不一定相等，使用等距分組易使組間差異不明顯，進而使資源分配不佳。本研究提出Ordinal Transformation改良方法搭配Global K-means分組方法，以持續抽樣所得到模型相關資訊搭配Global K-means分組方法更新分組。本研究進一步驗證在變換本研究所設定之Layout與JobSet的彈性製造系統環境下之高與低相關程度之多細緻度模型下，導入Ordinal Transformation改良方法可節省高細緻度模型抽樣次數的使用，尤其是相關程度低的模型透過不斷更新高細緻度模型預測值後重新分組再抽樣能夠顯著節省高細緻度模型抽樣次數。

In different field of research there are many different fidelity models exist which considerd different system features. However, different fidelity modles have its pros and cons. When there is high fidelity model, we can evaluate system more accurately, but it will cause time-consuming and will lead to higher cost in building the model. On the other hand, lower fidelity model may exist bias, but it’s better for faster evaluation and the performance can provide partial of trend between low and high fidelity models. As the result, when facing multi-fidelity models, it is important to enhance the efficiency of optimization.
In this research, we exploite Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) to solve the simultaneous scheduling problem of machines and automated guided vehicles (AGVs) in flexible manufacturing system (FMS). Based on the problem, we considering the zone-control and alternative machine features to setup the multi-fidelity model.
In MO2TOS, grouping method is one of main factors which may affect the quality of optimization significantly. In this research, Improved Ordinal Transformation is proposed and for use with Global K-means to update the group after every iteration of MO2TOS. Futher more, we verify this method in different FMS layout and jobset. From the experimental results, this method can significantly allocate resource effectively and save simulation resources for higher and lower correlation models.

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