EVOLUTIONARY ALGORITHM FOR DYNAMIC RESOURCE ALLOCATION AND ITS APPLICATIONS

In modern manufacturing, dynamic allocation of resources is key to enhancing production efficiency and competitiveness. With the advancement of intelligent manufacturing and Industry 4.0, the manufacturing environment has become increasingly complex and variable, making traditional static resource allocation methods insufficient for practical needs. This paper aims to explore multi-objective optimization methods for dynamic manufacturing resource allocation by constructing a multi-objective optimization model and proposing an improved NSGA-II algorithm to address this issue. The study demonstrates that the improved algorithm significantly enhances population diversity and global search capability, effectively coping with dynamic manufacturing environments to provide efficient and reliable resource allocation solutions. This research not only offers new insights into dynamic resource allocation but also serves as a valuable reference for related fields.