Imagen de Portada

Statistical Learning-Assisted Evolutionary Algorithm for Digital Twin-Driven Job Shop Scheduling with Discrete Operation Sequence Flexibility

Guardado en:
Publicado en:Symmetry vol. 17, no. 10 (2025), p. 1614-1634
Autor principal: Jia, Yan
Otros Autores: Cheng Weiyao, Meng Leilei, Zhang Chaoyong
Publicado:
MDPI AG
Materias:
Linear programming
Collaboration
Integer programming
Workshops
Optimization
Job shops
Manufacturing
Machine learning
Energy consumption
Evolutionary algorithms
Preventive maintenance
Factories
Scheduling
Search methods
Digital twins
Genetic algorithms
Knowledge
Decision making
Flexibility
Operators
Mixed integer
Industry 4.0
Industry 5.0
HyperText Markup Language
Job shop scheduling
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems.
ISSN:2073-8994
DOI:10.3390/sym17101614
Fuente:Engineering Database