Journal of Engineering, Project, and Production Management, 2027, 17(2), 2025-257
Intelligent Manufacturing Scheduling Optimization Based on Genetic Algorithm and MES
1 Lecturer and Oujiang Distinguished
Professor, School of Business, Wenzhou University, Wenzhou, 325035,
China
2 Associate Professor, School of Business, Henan University, Kaifeng,
475000, China, E-mail: tongxunzuozhe8866@126.com (corresponding author).
Project Management
Received November 4, 2025; revised May 18, 2026; July 15, 2026; accepted October 4, 2026
Available online October 20, 2026
Abstract: Against the backdrop of intensified global manufacturing competition and the deep advancement of Industry 4.0, production scheduling, as a core link in efficient operations within intelligent manufacturing, faces challenges that traditional methods can no longer meet, such as delayed data acquisition and insufficient dynamic response. Therefore, a scheduling model for Industry 4.0 intelligent manufacturing based on a Genetic Algorithm (GA) and a Manufacturing Execution System (MES) is proposed. During the process, the particle swarm algorithm was used to optimize the Genetic Algorithm's parameters serially, thereby reducing complexity. At the same time, the ant colony algorithm was integrated to optimize key processes, improving the model's stability and convergence and establishing a scheduling optimization model that integrates data collection, rapid processing, and reasonable allocation. The experimental results show that the inventory turnover rate of the intelligent manufacturing scheduling model constructed in this study has increased by 3.24%, 4.58%, and 6.17%, respectively, compared to the control model’s. Meanwhile, the on-time delivery rate reaches 92.13% and 91.75% across two datasets, both above 91%. The task delay rates are 7.69% and 8.67%, showing significant advantages over the comparison models at 10.45% and 11.75%, respectively. These results indicate that the proposed model can achieve efficient allocation of production resources and dynamic responsiveness, thereby improving production line efficiency and offering a new approach to intelligent manufacturing scheduling.
Keywords: Ant colony optimization (ACO); genetic algorithm (GA); hybrid metaheuristics; intelligent manufacturing; manufacturing execution system (MES); scheduling optimization.
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Citation: Xu, Z. and Wang, J. (2027). Intelligent Manufacturing Scheduling Optimization Based on Genetic Algorithm and MES. Journal of Engineering, Project, and Production Management, 17(2), 2025-257.
