Machine Learning-Based Model Predictive Control for Collaborative Production Planning Problem with Unknown Information

In industrial production planning problems, the accuracy of the accessible market information has the highest priority, as it is directly associated with the reliability of decisions and affects the efficiency and effectiveness of manufacturing. However, during a collaborative task, certain private information regarding the participants might be unknown to the regulator, and the production planning decisions thus become biased or even inaccurate due to the lack of full information. To improve the production performance in this specific case, this paper combines the techniques of machine learning and model predictive control (MPC) to create a comprehensive algorithm with low complexity. We collect the historical data of the decision-making process while the participants make their individual decisions with a certain degree of bias and analyze the collected data using machine learning to estimate the unknown parameter values by solving a regression problem. Based on an accurate estimate, MPC helps the regulator to make optimal decisions, maximizing the overall net profit of a given collaborative task over a future time period. A simulation-based case study is conducted to validate the performance of the proposed algorithm in terms of estimation accuracy. Comparisons with individual and pure MPC decisions are also made to verify its advantages in terms of increasing profit.