Paradigms in systems engineering, such as industrial processes, infrastructure management, and service assurance, are interrelated, and quickly adapt to the complexities of automated systems, helping them to achieve optimal performance. Technology breakthroughs related to cyber-physical systems, such as sensors and smart meters, are the key to real-time automated management of complex and interconnected services and infrastructure, providing efficient industrial processes as well as basic commodities and services such as energy, water, transportation, and telecommunications. These degrees of automation and system interconnection, both for physical and digital industry and infrastructure, generate new levels of complexity for which the methodology used should match the technological and the end-users’ requirements.
This Special Issue on “Modeling and Simulation of Complex Networks for Automation in Systems Engineering” aims to present novel advances on methodologies to improve the development and use of a complexity science framework for automated digital management of industry and infrastructure systems. In recent years, network science has become a popular approach to model complex systems. The latest advances in research related to network dynamics and structure provide an excellent framework to understand, control and predict complex systems, such as those related to Industrial, Manufacturing, Electrical, and Civil engineering. Network models which are specifically adapted to capture spatiotemporal dimensions of an engineering system, such as spatial networks and temporal networks, are of particular interest. New directions on graph signal processing and graph machine learning are providing innovative research in complex systems, blending powerful AI and data analytics tools with the graph-based structure of the problem.
The scope of this Special Issue includes (but is not limited to):
- Complexity science for systems engineering.
- Dynamics on networks and dynamics of networks.
- Decision-making support in complex systems.
- Diffusion processes and dynamics in complex networks.
- Swarm intelligence applications in networked systems.
- Intelligent infrastructure and asset management.
- Approaches and bounded strategies for learning in multi-agent systems at different scales.
- Multi-agent learning solutions for near-real time decision making.
- Automation in complex systems.
- Graph signal processing in engineering systems operations and management.
- Graph machine learning and graph neural networks models in systems operations and management.
- Sustainable supply chain management.
Dr. Silvia Carpitella
Dr. Manuel Herrera
Prof. Dr. Bruno Melo Brentan
Prof. Dr. Joaquín Izquierdo