基於深層學習的生產系統動態控制

周芳屹

doi:10.6342/NTU201701817

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基於深層學習的生產系統動態控制

Dynamic Control of Manufacturing System – A Deep Learning Approach

周芳屹 , Masters　　Advisor：吳政鴻　　

英文 DOI： 10.6342/NTU201701817

深層學習；動態規劃；馬可夫決策過程； Deep Learning ； Dynamic Programming ； Markov decision process

Abstract │ Reference (29) │ Altmetrics

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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.

Reference ( 29 ) 〈TOP〉

[1] R. Ferrero, J. Rivera, and S. Shahidehpour, "A dynamic programming two-stage algorithm for long-term hydrothermal scheduling of multireservoir systems," IEEE Transactions on Power Systems, vol. 13, pp. 1534-1540, 1998.
連結：
[2] T. Volling, D. E. Akyol, K. Wittek, and T. S. Spengler, "A two-stage bid-price control for make-to-order revenue management," Computers & Operations Research, vol. 39, pp. 1021-1032, 2012.
連結：
[3] H.-S. Ahn, I. Duenyas, and R. Q. Zhang, "Optimal stochastic scheduling of a two-stage tandem queue with parallel servers," Advances in Applied Probability, vol. 31, pp. 1095-1117, 1999.
連結：
[4] C.-H. Wu, M. E. Lewis, and M. Veatch, "Dynamic allocation of reconfigurable resources ina two-stage Tandem queueing system with reliability considerations," IEEE Transactions on Automatic Control, vol. 51, pp. 309-314, 2006.
連結：
[6] S. P. Meyn, "Workload models for stochastic networks: Value functions and performance evaluation," IEEE Transactions on Automatic Control, vol. 50, pp. 1106-1122, 2005.
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