A Model Predictive Control for Lot Sizing and Scheduling Optimization in the Process Industry under Bidirectional Uncertainty of Production Ability and Market Demand. (30th September 2022)

• Record Type: Journal Article
• Authors: Tang, Qi; Wang, Yiran
• Other Names: Precup Radu-Emil Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-30
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2676545
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : In the face of bidirectional uncertainty of market demand and production ability, this paper establishes a multiobjective mathematical model for lot sizing and scheduling integrated optimization of the process industry considering both material network and production manufacturing and finds the optimal decision of the model through model predictive control to minimize total completion time and total production cost. While realizing the model predictive control proposed in this paper, the Elman neural network predicts the relevant parameters required by learning historical orders for the uncertain market demand and equipment production ability. Then, the calculation formulas of product supply and demand matching and equipment production ability are formed and introduced into the next stage of the model as a constraint condition. In addition to the above constraints for constructing lot sizing and scheduling integrated models in the process industry, this paper also considers both the material network and production manufacturing and uses the IMOPSO algorithm to solve the problem iteratively. So far, a complete model predictive control can be generated. Through the model predictive control, the production system can respond in advance, make appropriate changes to offset the foreseeable interference, and obtain the lot sizing and scheduling scheme considering bidirectional uncertainty, thereby improving the system's overall robustness. Finally, this paper realizes the model's predictive control process through example simulation and analyzes the operation results combined with the scheduling Gantt chart to verify the applicability and effectiveness of the model.