本論文探討了應用基因演算法與類神經網路在電腦整合製造系統下的生產排程之研究，當中考慮了機台的分派及運送物料所需要的置頂式天車的搬運時間，以及在混合生產中為了配合後段製程之需求而指定前段製程的產出順序之限制。經過模擬發現，天車會因複雜的交通限制而產生塞車狀況，使得所需的搬運時間難以計算，必須仰賴模擬來獲得數據，而利用類神經網路可以取代耗時的模擬，並且快速又準確地提供天車的搬運時間，在基因演算法的優化過程中，將會大量使用到類神經網路來獲得數據，並且依照所需要之目標來找到最佳的生產排程，最後將排程的結果與模擬軟體進行比對，發現完工時間相近因而說明了準確的搬運時間對於排程結果的重要性。

Production scheduling by the integration of genetic algorithm (GA) and artificial neural network (ANN) in computer integrated manufacturing system is studied in this thesis, where the transportation time of overhead hoist transporter (OHT) is considered for optimal dispatching. The OHT transportation time varies from complicated traffic constraints that can only be obtained by simulation. Instead of the time-consuming simulation by common software, the transportation time of different machine dispatching is first estimated by an ANN model. GA is then integrated to validate the scheduling of minimal makespan and maximal production output. Numerical verifications show that the estimated transportation time paves the way for production scheduling in engineering applications. The proposed model integrating GA with ANN makes the optimal scheduling of OHT system become possible.

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