置頂式天車運用於製造業做搬運車輛是相當常見的，在運用天車系統的製造廠裡，排程能夠決定整廠的工作效率，在考慮了天車搬運時間下的排程能夠使得排程結果更為合理；也因為天車是循著軌道在運作的關係，天車常常會有塞車的情況發生導致搬運時間難以估算，更在模擬與建模後發現，天車的數量、產品的製程時間以及機台的分配為影響天車搬運時間的因素，若要獲得天車搬運時間勢必得仰賴模擬來獲得數據；在排程的最佳化過程中，通常會有大量的迭代運算，若是每一次都必須透過模擬來得到搬運時間，那麼勢必會耗費大量的時間以及操作，因此本論文提出使用類神經網路，在訓練過後，能夠立即且有效的預測出天車的搬運時間，用以提供排程最佳化使用。

It is known that most works on scheduling of manufacturing systems do not consider the transportation requirement. In systems with overhead hoist transporter (OHT), it is difficult to calculate the transportation time due to the influence of OHT block on different product process time, machine configuration and the number of OHT. In order to provide the transportation time precisely and immediately for optimal scheduling, the neural network has been developed to predict the transportation time in various situations. For job shop scheduling system of 10 machines, the neural network of 5 layers with 11 input and 10 output neurons can determine the transportation time efficiently for optimal scheduling.

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