Cloud-based architecture of digital twins applied to the optimal design of large-scale supply chains

Authors

DOI:

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

Keywords:

Azure Digital Twins, supply chain, network design, digital twin, PSO, MILP, python

Abstract

The design of large-scale supply networks is an NP-hard MILP problem that requires dynamic representation and efficient optimization in industrial contexts. This study proposed, implemented, and validated a four-layer cloud-based architecture that integrates Microsoft Azure Digital Twins (ADT), a multi-echelon MILP model, a hybrid PSO, and a Python-based integration engine with bidirectional synchronization. The main algorithmic contribution consisted of using the linear relaxation of the MILP as the PSO fitness function, providing a formal lower bound for the original problem. A total of 74 experimental runs were conducted using a capacity repair operator, achieving a zero-infeasibility rate. The evaluated instances, across S, M, and XL scales and five complexity levels, showed an inverse relationship between the integrality gap and the quality of the PSO solution. Likewise, instances with low combinatorial complexity achieved over 90% consistency in opening decisions, preserving the decision state of the digital twin. The proposed framework integrates cloud-based architecture, MILP optimization, and hybrid PSO for supply chains with experimental validation.

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Author Biographies

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.

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Published

2026-07-20

How to Cite

Marmolejo-Saucedo, J. A., Rodríguez-Aguilar, R., & Mendoza, A. (2026). Cloud-based architecture of digital twins applied to the optimal design of large-scale supply chains. Revista Científica De Sistemas E Informática, 6(2), e1682. https://doi.org/10.51252/rcsi.v6i2.1682

Issue

Vol. 6 No. 2 (2026): Revista Científica de Sistemas e Informática

Section

Original articles

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Copyright (c) 2026 José Antonio Marmolejo-Saucedo, Román Rodríguez-Aguilar, Abraham Mendoza

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