Research in Mathematics

Information Systems for Digital Twins in Logistics

The convergence of digital twins and information systems is transforming logistics by enabling real-time data integration, advanced simulations, and intelligent, data-driven decision-making. A digital twin is a virtual representation of physical logistics assets, systems, or processes, continuously updated through live data streams. This allows for enhanced visibility, performance monitoring, predictive analytics, and scenario testing within logistics networks.

Mathematically, digital twins in logistics draw upon a rich foundation of quantitative tools, including systems modelling, stochastic optimization, control theory, differential equations, agent-based modelling, and machine learning. These models enable the analysis of dynamic and complex logistics systems under uncertainty, supporting decision-making in areas such as fleet management, inventory control, demand forecasting, and route optimization.

This Article Collection for the Open Access journal Research in Mathematics aims to advance the mathematical foundations for information system architectures and algorithmic methods underpinning digital twins. The collection seeks high-quality contributions that apply rigorous mathematical modelling, simulation, and computational optimization techniques to real-world logistics challenges. We particularly welcome articles that develop novel algorithms, analytical frameworks, or digital architectures that enhance the efficiency, resilience, and sustainability of logistics systems.

Contemporary challenges in this field include integrating heterogeneous data sources, managing the computational complexity of high-fidelity simulations, and ensuring scalability in real-time decision support. Additionally, as logistics systems become more interconnected and data-rich, the need for robust mathematical tools to handle uncertainty, nonlinearity, and multi-objective trade-offs becomes increasingly vital (e.g., Pareto optimization in dynamic networks, stochastic control for demand uncertainty, and graph-theoretic methods for multimodal transport). This Article Collections aims to contribute to both the theoretical development and the practical deployment of digital twin-based logistics solutions, reinforcing the vital role of mathematics in shaping the future of intelligent logistics systems.

In today’s rapidly evolving supply chain environment, logistics systems must adapt to increasing complexity, uncertainty, and customer demands for speed and transparency. Traditional methods often fall short in providing real-time visibility and proactive decision-making. The integration of digital twins with information systems addresses these challenges by creating a live, data-rich representation of logistics operations that supports simulation, prediction, and optimization. This fusion enables stakeholders to detect disruptions early, assess alternative scenarios, and implement timely corrective actions.

It also fosters greater agility, cost-efficiency, and sustainability by improving asset utilization and reducing waste. Recent work highlights how digital twins enable resilient supply chains (Ivanov & Dolgui, 2021), predictive maintenance (Kritzinger et al., 2018), and real-time network reconfiguration (Lu et al., 2022). As industries move toward smart logistics and Industry 4.0, understanding how to design and deploy digital twin-enabled information systems is critical. Advancing this field contributes not only to operational excellence but also to strategic advantages in a highly competitive global market. Therefore, research in this area is both timely and essential for shaping the future of intelligent logistics.

This Article Collection's main focus is on the mathematical and computational foundations of digital twin-enabled information systems in logistics, addressing a timely intersection of operations research, optimization, and applied mathematics. We invite original research articles, review papers, and case studies that contribute to the modelling, simulation, and optimization of logistics systems using digital twin technologies. Relevant subtopics include, but are not limited to: mathematical modelling of logistics networks (graph theory, queuing theory, and network optimization; e.g., Crainic & Laporte, 2022); simulation-based optimization (e.g., stochastic and robust optimization under uncertainty; see Birge & Louveaux, 2011); dynamic systems and real-time analytics (control theory, Kalman filtering, and adaptive learning approaches); predictive modelling and decision support systems (machine learning, Bayesian networks, and hybrid simulation-optimization); integration of information systems with digital twin architectures (ontology-driven modelling, data fusion, and knowledge graphs; see Tao et al., 2019); and applications of operations research in intelligent logistics (combinatorial optimization, multi-objective optimization, and scheduling).

Submissions that apply advanced mathematical techniques such as combinatorics, stochastic differential equations, graph neural networks, or agent-based stochastic modelling to real-world logistics challenges are especially welcome. The issue also encourages interdisciplinary contributions bridging mathematics, engineering, and computational science. Through rigorous mathematical approaches, this collection aims to advance the theory and practice of smart logistics and align with the journal’s mission of publishing impactful research in applied and interdisciplinary mathematics.

Keywords: Digital Twins, Information Systems, Logistics Optimization, Simulation-Based Modelling, Mathematical and Computational Methods

Manuscript Submissions:

Manuscript submission is open until 31st May 2026.

Please carefully review the journal scope and author submission instructions prior to submitting an abstract as it will be rejected if it does not fall within the scope of the journal.

All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Manuscripts which do not fall within the scope of the journal will be rejected.

To submit your papers to this Article Collection, please:

1. Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the relevant Article Collection from the drop-down menu under the question, "Information Systems for Digital Twins in Logistics"

We are able to offer a 10% Discount to all authors, and have a limited number of 20% Discount codes only available for early submissions. It should be noted that discount codes must be entered in at the point of submission as they cannot be applied retroactively, nor can these be combined as only the higher valued discount would be applicable.

Please contact Christopher Montgomery, Commissioning Editor regarding details on obtaining your discount codes, and with any other queries for this Article Collection.

Article Collection Guest Advisors

Dr. Seyed Mojtaba Sajadi (SFHEA) is Associate Professor in Operations and Supply Chain Simulation in the Operations and Information Management Department at Aston Business School. His research focuses on simulation-based optimization in supply chain management and business analytics. He has extensive expertise in various simulation techniques, including Discrete Event Simulation, Agent-Based Simulation, System Dynamics, Monte Carlo methods, and Hybrid Simulation. His work applies these techniques across diverse fields such as project management, healthcare, manufacturing systems, supply chain management, disaster management, banking services, ICT, and after-sales services. Additionally, he has a strong background in optimization, business analytics, and the use of mathematical modelling, metaheuristic algorithms, and data science techniques to develop effective simulation-based optimization approaches for solving complex real-world problems.

Prof. Jeremy Laliberté is a Full Professor in the Department of Mechanical and Aerospace Engineering at the University of Carleton, Canada. His research interests include processing and testing of composites and fibre metal laminate airframe materials, design of uninhabited aerial vehicles (UAVs) and micro aerial vehicles (MAVs), low velocity impact modelling and testing for composites, and biomimetic and bio-inspired structures for air vehicles.

Dr. Mohammad Daneshvar Kakhki is an Assistant Professor of Computer Information Systems at California State University, Long Beacch. He holds a doctorate in Information Systems from The University of North Carolina at Greensboro and a Master of Science in Industrial Engineering from Sharif University of Technology, Iran. His research is on the intersection of information systems and supply chain management, and he studies topics such as implications of business intelligence and data analytics, complex interorganizational interactions, and interorganizational systems. The methodological tools that he uses for his research include survey research, agent-based simulation, and meta-analysis. He has published in such journals as Information & Management, Communications of the Association for Information Systems, Enterprise Information Systems, International Journal of Production Research, and Journal of Electronic Commerce Research.

Dr. Mahshid Tootoonchy is a scholar affiliated with Victoria University, specializing in logistics, project and operations management, and simulation and optimization. Her research encompasses the dynamics of project management offices, intelligent decision-making systems, and the application of machine learning in logistics and production systems. Dr. Tootoonchy has contributed to various interdisciplinary studies, including environmental policy modelling and hyperspectral imaging in agriculture. Her work is recognized in journals such as the International Journal of Project Management, Expert Systems with Applications, and the International Journal of Production Economics.