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A Combined Model Predictive Control And Time Series Forecasting Framework For Production-inventory Systems

机译：生产库存系统的组合模型预测控制和时间序列预测框架

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摘要

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.
机译：模型预测控制（MPC）先前已应用于供应链问题，并取得了可喜的结果。但是，到目前为止，已提出的大多数系统都没有关于未来需求的信息。在MPC框架中结合预测方法可以通过提供对未来的洞察来提高控制措施的效率。在本文中，通过为基于MPC和神经网络时间序列预测模型的生产库存系统提供完整的管理框架，探索了这种可能性。为了评估该方法的效率以及预测精度对整体控制性能的影响，对工业数据对提出的框架进行了测试。为此，将所提出的方法与在相同工业数据集上实施的几种替代性预测方法进行了比较。结果表明，由于为实时解决的MPC优化问题的公式提供了更准确的预测，因此该方案可以显着提高生产库存系统的性能。

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