彈性製造系統中零件加工途程的決定，通常於規劃的階段完成，在製造系統發生突發狀況時（例如機台當機）往往無法即時的根據現場生產及機台狀況，做零件途程或機台選擇的調整。因此，本研究提出的生產現場的動態零件途程指派模式，來解決上述的問題。現場的動態零件途程指派模式，包括製程OR 圖矩陣產生模組、途程決策模組及顏色時間派翠網路模擬及控制模組。製程OR圖矩陣產生模組，將製程計劃資料換成製程OR圖矩陣。接著，途程決策模組利用基因演算法，在最短剩餘加工時間為目標下，來搜尋下一階段的加工機台。完成機台選擇決策後，更新製程OR圖矩陣，並將決策的訊息傳至顏色時間派翠網路現場模擬及控制模組進行加工作業的指派。最後，以一個多途程彈性製造系統為例，驗證結合派翠網路及基因演算法的現場動態途程指派模式的效益。

In general, the tasks of scheduling in a flexible manufacturing system
(FMS) are performed at the planning stage and thus these systems can not
respond to the need for decision-making to select alternate machines and
alternate sequences when unexpected events, such as machine breakdown,
take place in the manufacturing system. Therefore, it is important to construct a dynamic dispatching model to handle the part routing problem in the FMS. In this research, a combined Genetic Algorithm (GA) and Colored Timed Petri Nets (CTPNs) approach, to construct a dynamic dispatching model for flexible part routes in a shop floor control system is proposed. The proposed dynamic dispatching model includes three modules:a generation module to produce a part OR graph matrix, a decision module for part routes and a CTPN simulation and control module. The
generation module for the part OR graph matrix generates part OR graph
matrices based on the process plans of parts. Next, the GA is introduced
to search for the next appropriate machine to process the part from the part OR graph matrix based on the shortest remaining processing time. After the machine for the next operation is selected, the part OR graph matrix is updated and the CTPN simulation and control module performs the tasks of dispatching resources for the next operation. Finally, a flexible manufacturing system with flexible part routes is given as an example to illustrate the performance of the proposed approach.