Multi-Objective Optimization Method for Dynamic Scheduling in 3D Printing Based on Improved NSGA-II - Luo , Longkun; Guo, Liang; Mou, Lixu (Scientific Journal of Technology, 2026)
To address unreasonable scheduling in distributed 3D printing systems under dynamic disturbances, an event-driven multi-objective dynamic scheduling optimization method is proposed. First, a model minimizing makespan and total processing cost is constructed, defining disturbance triggers and rescheduling criteria. Second, an improved NSGA-II algorithm is designed, featuring a chromosome segmentation mechanism to coordinate local and global optimization, adaptive operators for convergence efficiency, and a cost-aware greedy mutation strategy to enhance solution quality. Simulations show that, compared to traditional right-shift rescheduling, the proposed method significantly reduces makespan and slightly lowers total cost in equipment failure scenarios, offering an efficient solution for cloud manufacturing platforms.