Abstract
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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Random variance to the benchmark values. These results are plotted in this figure as the blue and red curves, respectively. These results suggest that the machine learning (ML)-based model predictive control (MPC) decisions are able to increase the profit by over 10% compared to the profit of the individual biased decisions
This paper focuses on the exact production planning problem with a collaborative mode and unknown information and proposes a machine learning-based MPC algorithm to solve this problem
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Some existing works in the literature—e.g., [38,39,40]—handle the production planning problem with uncertain variables, their problem formulations were different from that discussed in this paper with disembedded machine learning or MPC methods This machine learning-based MPC method is capable of extracting the target information from a complex but real historical data set and carrying out the estimation procedure in an off-line fashion. A gradient descent machine learning procedure with an adaptive learning scheme is developed to estimate the unknown parameters of the revenue in Q using historical data via solving a regression problem; An MPC method uses the estimated values of Q as its user-defined weight factors to predict the optimal decisions to maximize net profit; Electronics 2021, 10, 1818. Is a loss function, Z is the set of integer numbers, N is the set of non-negative integer numbers, s is the production demand, Q is the weighting parameters of the revenue, R is the weighting parameters of the productivity effort, P is the decision bias parameters of the participants, Π is the benchmark of the unknown parameters
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