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An Enhanced NSGA-II Driven by Deep Reinforcement Learning to Mixed Flow Assembly Workshop Scheduling System with Constraints of Continuous Processing and Mold Changing

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Publicado en:Systems vol. 13, no. 8 (2025), p. 659-679
Autor principal: Yang Bihao
Otros Autores: Chen, Jie, Xiao Xiongxin, Li, Sidi, Teng, Ren
Publicado:
MDPI AG
Materias:
Work stations
Space exploration
Integer programming
Workpieces
Deep learning
Molds
Optimization
Batch processing
Multiple objective analysis
Manufacturing
Batch production
Heuristic
Efficiency
Optimization models
Assembly lines
Scheduling
Continuous casting
Operators (mathematics)
Genetic algorithms
Searching
Solution space
Algorithms
Constraints
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Resumen:Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an excellent scheduling scheme is a challenging problem. This study addresses the mixed-flow assembly shop scheduling problem, considering continuous processing and mold-changing constraints, by developing a multi-objective optimization model to minimize additional production time and customer waiting time. As this NP-hard problem poses significant challenges in solution space exploration, the conventional NSGA-II algorithm suffers from limited local search capability. To address this, we propose an enhanced NSGA-II algorithm (RLVNS-NSGA-II) integrating deep reinforcement learning. Our approach combines multiple neighborhood search operators with deep reinforcement learning, which dynamically utilizes population diversity and objective function data to guide and strengthen local search. Simulation experiments confirm that the proposed algorithm surpasses existing methods in local search performance. Compared to VNS-NSGA-II and SVNS-NSGA-II, the RLVNS-NSGA-II algorithm achieved hypervolume improvements ranging from 19.72% to 42.88% and 12.63% to 31.19%, respectively.
ISSN:2079-8954
DOI:10.3390/systems13080659
Fuente:Advanced Technologies & Aerospace Database