力態地給予一個彈性製造系統適合的派工報則可以增加產出、降低平均流程時間、及減少延遲工件等。為了達到這個目的，即時的系統顯著資訊必須擷取，然後依據萃取出的資訊建立派工機制。兩個重要的議題主導著動態排程彈性製造系統的性能；其一是如何選取有用的系統屬性，另外就是派工機制的設計。本論文目標在於解決第一個議題。 一個好的屬性方法必須要提供哪些屬性該選擇，哪些該捨去的資訊。因此，在本文中提出了一個以模糊理論及基因演算法為基楚的監督式屬性探勘演算法(supervised attribute mining algorithm, SAMA)來處理上述的問題。此演算法可以依照各屬性彼此之間關聯程度的重要性來給予排序。有實驗利用模擬軟體建立了一個彈性製造系統來驗證此演算法的有效性實驗結果指出SAMA可以達到屬性評估跟最佳化的屬性子集點選取等目的。更進一步，利用所得出的最佳化屬性子集合與未經過SAMA處理的系統全部屬性來做比較。最後，結果顯非經由SAMA選擇的屬性當成排程器的輸入可以提升彈性製造系統的性能。

　　　　　Assigning proper dispatching rules dynamically has been shown to enhance various performance measures for a flexible manufacturing system (FMS). To achieve this, real-time salient information of the system is extracted and then a rule’s dispatching mechanism is built for the scheduling task. For a dynamic scheduled FMS, two critical issues dominate the performance; the first is the selection of system attributes and the second is the design of the dispatching mechanism. The paper aims to deal with the first issue. A good attribute evaluation method should provide the information from which attribute are selected or removed. In this paper, a supervised attribute mining algorithm (SAMA), which is based on the fuzzy set-theoretic approach and genetic algorithm (GA), is proposed to execute this function. SAMA is able to rank attributes according to their relative importance. In the experiment, a FMS is conducted to demonstrate the validity of the proposed SAMA. The experimental results indicate that the attribute evaluation task and optimal attribute subset selection can be achieved by using the SAMA. Moreover, compared with using all system attributes without selection, performance of the FMS can be improved by using the optimal attributes as input of the scheduler.