With the gradual diversification of customer demand, to improve the rapid response ability of enterprises, this paper fully considers uncertain market information under the background of mass customization and establishes a new process industry multi-product switching production lot sizing and scheduling model with the goal of minimizing the maximum completion time and total switching cost. Fuzzy chance-constrained programming is used to explicitly incorporate market demand with uncertain quantities into the model. Starting from reality, this paper considers the switching cost of equipment when processing multiple varieties of products and skillfully integrates the conversion rate of materials during processing into the novel model, making the entire production system closer to the real state. It provides a new concept to consider cost reduction for actual workshop scheduling management. In addition, this paper proposes an improved multi-objective genetic particle swarm optimization (SMOPSO-IIs) algorithm to simulate and solve the model. The operation results show that the Pareto solution obtained by the SMOPSO-IIs algorithm is better overall. Finally, the model is solved by example simulation, and the operation results are analyzed along with a scheduling Gantt chart to verify its applicability and effectiveness. The model presented in this paper can be used to further shorten the gap between production theory and practical application and improve the current workshop scheduling management system of the process industry.INDEX TERMS Mass customization, process industry lot sizing and scheduling; uncertain market information; product switching

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.