Application of Process Industrial Mathematical Scheduling Model in Enterprise Production -Based on Decomposed Multi-Objective Evolution

Abstract

The production scheduling problem in process industries is a complicated issue of complex multi-objective optimizing that has a significant impact on the production efficiency and economic benefits of enterprises. However, traditional scheduling methods often fail to meet the multi-objective optimization requirements in complex production environments. For improving the production efficiency and economical benefit of process industry enterprises, this study constructs a production scheduling model and solves it using a decomposed multi-objective evolution method. When solving the model using the decomposed multi-objective evolution method, this study also proposes an improvement using self-organizing mapping. The results show that when the optimization objectives are the max completing time and production switches number, the proposed method has a high convergence speed and HV value. The algorithm achieves convergence after 2100 evaluations with an HV value of approximately 0.302. The HypE algorithm achieves convergence after 2400 evaluations with a Hypervolume value of approximately 0.284. The algorithm also exhibits a high level of diversity, with an IGD value of approximately 0.672, which is higher than the other algorithms. The proposed algorithm demonstrates high convergence and diversity when solving the production scheduling model.