Programación de Operaciones en Manufactura Inteligente: Estado del Arte de la Industria 4.0

Agostino, I., Ambrogio, G., & Di Lorenzo, R. (2020). Using a digital twin for production planning and control in Industry 4.0. Procedia Manufacturing, 42, 543–548.

Alexopoulos, K., Chryssolouris, G., & Michalos, G. (2024). Adaptive production scheduling in smart manufacturing systems using deep reinforcement learning and discrete event simulation. Applied Sciences, 15(1), 232.

Alvarez-Tinajero, N., Basantes-Andrade, A., Ayala-Vásquez, O., Pereira-González, L. M., & Arciniegas-Romero, G. (2026). Mathematical Competencies and Critical Thinking in Secondary Education: A PRISMA-Based Systematic Review (2019–2025). F1000Research, 14, 1407.

Da Silva, L. R. R., Pereira, T. M., & Gonçalves, C. A. (2025). A review on Digital Twin technologies for machining processes in Industry 4.0. Journal of Manufacturing and Materials Processing, 9(7), 211.

Del Real Torres, A., Fernández-Ahumada, E., & García-Peñalvo, F. J. (2022). A review of deep reinforcement learning approaches for smart manufacturing in Industry 4.0 and 5.0 frameworks. Applied Sciences, 12(23), 12377.

Echeverria, I., Arteaga, I., & Egana, J. (2023). Graph-based deep reinforcement learning for large-scale flexible job shop scheduling. arXiv. https://arxiv.org/abs/2310.15706

Ivanov, D., Sokolov, B., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Transportation Research Part E: Logistics and Transportation Review, 142, 102190.

Khadivi, P., Charter, A., Yaghoubi, S., Jalayer, M., Ahang, M., Shojaeinasab, A., & Najjaran, H. (2023). Deep reinforcement learning for scheduling in smart manufacturing: A comprehensive review. arXiv. https://arxiv.org/abs/2310.03195

Latsou, C., Mavrikios, D., & Chryssolouris, G. (2024). A unified framework for scalable and reusable Digital Twin development. IFAC-PapersOnLine, 57(7), 1335–1341.

Moon, T., Lee, K., & Park, J. (2024). Heterogeneous graph-based reinforcement learning for flexible job shop scheduling. arXiv. https://arxiv.org/abs/2402.08979

Ngwu, C. S., Zhang, Y., & Wu, C. (2025). Artificial intelligence techniques in production scheduling: A systematic review. Journal of Intelligent Manufacturing. Advance online publication.

Nievas, N., Moreira, M., & Oliveira, E. (2024). A review of reinforcement learning applications in manufacturing systems. Journal of Intelligent Systems, 33(2), 456–479.

Rossit, D. A., Nesmachnow, S., & Márquez, A. (2024). Metaheuristic approaches for multi-objective flow shop scheduling with missing operations. arXiv. https://arxiv.org/abs/2407.15802

Zhang, R., Wang, M., & Fan, T. (2023). Flexible scheduling for human–machine collaborative workshops based on a hierarchical digital twin and reinforcement learning. Flexible Services and Manufacturing Journal.

Zhang, C., Zhang, J., Lim, A., & Ong, Y. S. (2020). Learning dispatching rules via deep reinforcement learning. arXiv. https://arxiv.org/abs/2010.12367