對於製造業而言，生產排程是相當重要的環節，其主要探討內容為在有限的資源中，藉由工作順序以及機台分配，降低生產成本提供企業的經濟效益以及競爭力。然而，為因應大環境變動，排程問題也逐漸趨於複雜，機台、工作量也愈來愈龐大，良好的生產排程計畫的制定，可以避免時間、成本的浪費，達到利潤的最大化，生產排程本身是一個複雜的問題，很難找到最佳排程方法且求解費時。因此，本研究先以不同的類神經網路方法進行排程，解決流程式生產排程問題，以最小化總完工時間為目標，借助其學習能力進行排程，並將其與基因演算法做結合，使其獲得更好的結果。首先蒐集排程問題之標竿問題及最佳解，以供產生進行學習之樣本，依據不同的類神經網路方法，將排程問題之特徵擷取出來，再運用獲得之特徵進行訓練，在面對新的排程問題時，使用訓練好的類神經網路方法先獲得起始解再將其放入基因演算法中將工作進行排序。結果顯示在單純使用類神經網路的方法中，使用卷積神經網路可以獲得較佳的結果，而利用卷積神經網路為基礎之基因演算法跟所有方法進行比較時，擁有最好的結果。

Scheduling is the most crucial part of production management. It is the process of arranging, controlling, and optimizing works in a production process. Enterprises can reduce cost and enhance competitiveness with scheduling important resources. Flow shop scheduling is a commom system for modern factories. Nowadays, the process technology evolves and manufacturing environments become more complicated. As the scale of manufacturing systems grows, namely more jobs and machines are involved, those problems become more difficult and time-consuming to find the best solution. In this study, we start by solving a flow shop scheduling problem with neural networks models. The goal is to find a sequence of different jobs to minimize makespan. We propose three models to solve flow shop scheduling by deep neural networks (DNN), support vector regression (SVR), and convolutional neural network (CNN), respectively. Then we combine neural networks models with the genetic algorithm (GA) to obtain neural networks based genetic algorithm. First, we extract features from benchmark for flow shop scheduling problems to train neural networks models. Next, we solve the new flow shop scheduling problems with trained neural networks models. Then the sequence obtained from the neural network models is used to generate the initial population for the genetic algorithm. The makespan of the sequence obtained by above approaches compare with each other, back propagation neural network (BPN), GA and dispatching rules. It was found that CNN approach performs better than other neural networks models and CNN based genetic algorithm get the best performance.

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