Arquitectura cloud-based de gemelos digitales para el diseño óptimo de cadenas de suministro de gran escala

Jose Antonio Marmolejo-Saucedo; Román Rodríguez-Aguilar; Abraham Mendoza

doi:10.51252/rcsi.v6i2.1682

Autores/as

José Antonio Marmolejo-Saucedo Universidad Panamericana https://orcid.org/0000-0002-8539-9828
Román Rodríguez-Aguilar Universidad Panamericana https://orcid.org/0000-0002-6496-4453
Abraham Mendoza Universidad Panamericana https://orcid.org/0000-0001-8496-4779

DOI:

https://doi.org/10.51252/rcsi.v6i2.1682

Palabras clave:

Azure Digital Twins, cadena de suministro, diseño de red, gemelo digital, PSO, MILP, python

Resumen

El diseño de redes de suministro de gran escala es un problema MILP NP-duro que requiere representación dinámica y optimización eficiente en contextos industriales. Este estudio propuso, implementó y validó una arquitectura cloud-based de cuatro capas que integra Microsoft Azure Digital Twins (ADT), un modelo MILP multi-escalón, un PSO híbrido y un motor de integración en Python con sincronización bidireccional. La principal contribución algorítmica consistió en emplear la relajación lineal del MILP como función de aptitud del PSO, proporcionando una cota inferior formal del problema original. Se ejecutaron 74 corridas experimentales con un operador de reparación por capacidad, alcanzando una tasa de infactibilidad nula. Las instancias evaluadas, en escalas S, M y XL y con cinco niveles de complejidad, evidenciaron una relación inversa entre la brecha de integralidad y la calidad de la solución PSO. Asimismo, las instancias de baja complejidad combinatoria lograron una consistencia superior al 90% en las decisiones de apertura, preservando el estado decisional del gemelo digital. El marco propuesto integra arquitectura cloud-based, optimización MILP y PSO híbrido para cadenas de suministro con validación experimental.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Román Rodríguez-Aguilar, Universidad Panamericana

Prof. Dr. Roman Rodriguez-Aguilar earned his Ph.D. from the School of Economics at the National Polytechnic Institute in Mexico. He also holds a master’s degree in Engineering from the National Autonomous University of Mexico (UNAM), another master’s degree in Administration and Public Policy from the Monterrey Institute of Technology and Higher Education, a postgraduate degree in Applied Statistics from the Research Institute in Applied Mathematics and Systems at UNAM, and a bachelor’s degree in Economics from UNAM. Currently, he is a professor at the School of Economic and Business Sciences at Universidad Panamericana in Mexico. His research interests include large-scale mathematical optimization, statistical learning, computational intelligence, health and energy economics, digital transformation in organizations, and causal artificial intelligence. He has taught at several prestigious public and private universities in Mexico and has supervised numerous master’s and Ph.D. students. Additionally, he has delivered many lectures, courses, and workshops internationally. Prof. Rodriguez has co-authored numerous research articles published in journals indexed by the Science Citation Index, as well as chapters and books with recognized publishers. He has also coordinated various research projects and is a Level II member of the National System of Researchers in Mexico.

Abraham Mendoza, Universidad Panamericana

Abraham Mendoza Andrade is a Full Professor (Category D) at the Faculty of Engineering of the Pan-American University, Guadalajara campus, since 2008. He holds a Bachelor's degree in Industrial Engineering from the Pan-American University, graduating Magna Cum Laude. He has a Master's degree with a dual degree in Industrial Engineering and Operations Research and a PhD with a dual degree in the same disciplines from Pennsylvania State University. He worked as a consultant in process and operations optimization for various companies in Guadalajara and completed a professional internship at Bayer MaterialScience in Pittsburgh, where he developed the SCOR model in the area of Operations and Supply Chain. He was an assistant professor at Pennsylvania State University. His research interests include inventory theory, supply chain optimization, transportation, and materials handling. He has over 30 scientific publications in high-impact international journals. He is a Level 1 member of the National System of Researchers and a member of IISE and INFORMS.

Citas

Avella, P., Calamita, A., & Palagi, L. (2023). A computational study of off-the-shelf MINLP solvers on a benchmark set of congested capacitated facility location problems. ArXiv. https://doi.org/10.48550/arXiv.2303.04216

Dominguez Martinez, V., Parody De La Cruz, J. D., Guerra Manjarrez, J. F., & Rodriguez Velandia, D. A. (2026). Digital twins in healthcare (2018–2024): A scientometric analysis with emphasis on cardiology and perspectives for Colombia. En M. Zuluaga (Ed.), Scientometric Perspectives on Biomedical Research: Applications Across Oncology, Infectious Diseases, Bioinformatics, and Medical Technologies (Vol. 1, pp. 33-50). Contemporary Biomedical Research. Pro-Metrics. https://doi.org/10.47909/978-9916-9331-7-6.5

Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92–118. https://doi.org/10.1016/j.omega.2013.08.006

Geoffrion, A. M., & Graves, G. W. (1974). Multicommodity Distribution System Design by Benders Decomposition. Management Science, 20(5), 822–844. https://doi.org/10.1287/mnsc.20.5.822

Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication.

Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (pp. 85–113). Springer International Publishing. https://doi.org/10.1007/978-3-319-38756-7_4

Guo, D., & Mantravadi, S. (2025). The role of digital twins in lean supply chain management: review and research directions. International Journal of Production Research, 63(5), 1851–1872. https://doi.org/10.1080/00207543.2024.2372655

Hart, W. E., Watson, J.-P., & Woodruff, D. L. (2011). Pyomo: Modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 219–260. https://doi.org/10.1007/s12532-011-0026-8

Ivanov, D., & Dolgui, A. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086

Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968

Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In 1997 IEEE International Conference on Systems, Man, and Cybernetics: Computational Cybernetics and Simulation (Vol. 5, pp. 4104–4108). IEEE. https://doi.org/10.1109/ICSMC.1997.637339

Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474

Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313–1337. https://doi.org/10.1007/s10845-019-01512-w

Longo, F., Mirabelli, G., Padovano, A., & Solina, V. (2023). The Digital Supply Chain Twin paradigm for enhancing resilience and sustainability against COVID-like crises. Procedia Computer Science, 217, 1940–1947. https://doi.org/10.1016/j.procs.2022.12.394

Marmolejo-Saucedo, J. A. (2020). Design and Development of Digital Twins: A Case Study in Supply Chains. Mobile Networks and Applications, 25(6), 2141–2160. https://doi.org/10.1007/s11036-020-01557-9

Marmolejo-Saucedo, J. A. (2022). Digital Twin Framework for Large-Scale Optimization Problems in Supply Chains: A Case of Packing Problem. Mobile Networks and Applications, 27(5), 2198–2214. https://doi.org/10.1007/s11036-021-01856-9

Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management - A review. European Journal of Operational Research, 196(2), 401–412. https://doi.org/10.1016/j.ejor.2008.05.007

Microsoft. (2023). Digital Twins Definition Language (DTDL), Version 3. In Azure Open Digital Twins. https://azure.github.io/opendigitaltwins-dtdl/DTDL/v3/DTDL.v3.html

Microsoft. (2025a). Azure Digital Twins Core client library for Python (azure-digitaltwins-core). In Python Package Index (PyPI) (1.3.0). Python Software Foundation. https://pypi.org/project/azure-digitaltwins-core/

Microsoft. (2025b). What is an ontology in Azure Digital Twins? In Microsoft Learn. https://learn.microsoft.com/en-us/azure/digital-twins/concepts-ontologies

Microsoft. (2025c). What is Azure Digital Twins? In Microsoft Learn. https://learn.microsoft.com/en-us/azure/digital-twins/overview

Milenkovic, M. (2022). Internet of Things: System Reference Architecture. ArXiv. https://doi.org/10.48550/arXiv.2204.01872

Noel, M. M., & Jannett, T. C. (2004). Simulation of a new hybrid particle swarm optimization algorithm. In Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory (pp. 150–153). IEEE. https://doi.org/10.1109/SSST.2004.1295638

Pérez-Baquero, R.-S., Murgas Gutierrez, J. P., Arzuaga Gomez, A. C., & Suarez Ravelo, C. D. (2026). Decision technologies in control engineering: A scientometric review of LQR control for inverted pendulum systems (2006–2025). DecisionTech Review, 6, 1–14. https://doi.org/10.47909/dtr.36

Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings; IEEE World Congress on Computational Intelligence (pp. 69–73). IEEE. https://doi.org/10.1109/ICEC.1998.699146

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies (3rd ed.). McGraw-Hill/Irwin.

Somma, A., Amalfitano, D., De Benedictis, A., & Pelliccione, P. (2025). TwinArch: A digital twin reference architecture. Journal of Systems and Software, 112613. https://doi.org/10.1016/j.jss.2025.112613

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186

Tirkolaee, E. B., Goli, A., Faridnia, A., Soltani, M., & Weber, G.-W. (2020). Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms. Journal of Cleaner Production, 276, 122927. https://doi.org/10.1016/j.jclepro.2020.122927

Wasi, A. T., Anik, M. A., Rahman, A., Hoque, M. I., Islam, M. D. S., & Ahsan, M. M. (2025). A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization. ArXiv. https://doi.org/10.48550/arXiv.2504.03692