Multi-Objective Optimization Method for Dynamic Scheduling in 3D Printing Based on Improved NSGA-II

Longkun Luo; Liang Guo; Lixu Mou

doi:10.54691/9ddhs704

Authors

Longkun Luo
Liang Guo
Lixu Mou

DOI:

https://doi.org/10.54691/9ddhs704

Keywords:

3D Printing; Dynamic Scheduling; Multi-Objective Optimization; NSGA-II.

Abstract

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.

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References

[1] Zhai Y, Lados DA, LaGoy JL. Additive manufacturing: Making imagination the major limitation. JOM 2014; 66:808–16.

[2] Zhang J, Ding G, Zou Y, et al. Review of job shop scheduling research and its new perspectives under industry 4.0. J Intell Manuf 2019; 30:1809–30.

[3] Zeng L, Shi J, Li Y, et al. A strengthened dominance relation NSGA-III algorithm based on differential evolution to solve job shop scheduling problem. Comput Mater Contin 2024; 78:375–92.

[4] Ouyang HC, Zhang TR, Wu DH. Multi-objective Flexible Job Shop Scheduling Based on Improved NSGA-III Algorithm. Control Engineering of China 2023;30:105–12.

[5] Li XH, Wang XR, Zhao Y, et al. Dynamic Scheduling in Cloud Manufacturing Environment. Computer Systems & Applications 2021; 30:225–31.

[6] Darwish LR, El-Wakad MT, Farag MM. Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services. J Manuf Syst 2021; 61:196–209.

[7] He J, Wu J, Siau KL. Task scheduling strategy for 3DPCP considering multidynamic information perturbation in green scene: J Glob Inf Manag 2024; 32: 1–23.

[8] Poudel L, Zhou W, Sha Z. Resource-constrained scheduling for multi-robot cooperative three-dimensional printing. J Mech Des 2021; 143: 072002.

[9] He J, Wu J, Ni J, et al. Real-time task scheduling strategy for 3D printing cloud platforms in health scenes. Appl Intell 2025; 55:1002.

[10] Gao K, Yang F, Zhou M, et al. Flexible job-shop rescheduling for new job insertion by using discrete jaya algorithm. IEEE Trans Cybern 2019; 49: 1944–55.

[11] Stevenson Z, Fukasawa R, Ricardez-Sandoval L. Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant. J Sched 2020; 23:397–410.

[12] Mejía G, Montoya C, Bolívar S, et al. Job shop rescheduling with rework and reconditioning in industry 4.0: An event-driven approach. Int J Adv Manuf Technol 2022; 119: 3729–45.