Dynamic Control of Manufacturing System – A Deep Learning Approach

• Main Authors: Fang-Yi Zhou, 周芳屹
• Other Authors: Cheng-Hung Wu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/77898143255219806841

碩士 === 國立臺灣大學 === 工業工程學研究所 === 105 === This study presents a dynamic approach method for manufacturing systems by combing dynamic programming (DP) with deep learning. Due to the model complexity, dynamic programming cannot efficiently find optimal control policies for large systems. However, deep neuron network can now be used to predict control rules for a large scale of states. In this research, we consider a production system with reliability uncertainties and the objective is to minimize the average queue length. We construct an optimal policy space by combing an set of smaller scale systems. Then we apply the optimal policy space to train the deep neuron network as our policy predictor. The accuracy of DNN is validated by the k-fold cross-validation (k-cv) test in a wide variety of manufacturing systems. Then, discrete simulation is used to verify the cost different between near-optimal policies from deep learning and optimal policies from dynamic programming. Our result shows the near-optimal police output by deep neuron network high degree of accuracy as optimal dynamic police and the difference in simulation results is minimal.