Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

This article proposes a framework to analyse traffic-data processes on a long-haul backbone infrastructure network providing internet services at a national level. This type of network requires low latency and fast speed, which means there is a large demand for research focusing on near real-time decision-making and resilience assessment. To this aim, this article proposes two innovative, complementary procedures: a multi-view approach for the topology analysis of a backbone network at a static level and a time-series mining approach of the graph signal for modelling the traffic dynamics. The combined framework provides a deeper understanding of a backbone network than classical models, allowing for backbone network optimisation operations and management at near real-time. This methodology was applied to the backbone infrastructure of a major UK internet service provider. Doing so increased accuracy and computational efficiency for detecting where and when anomalies and pattern irregularities occur in the network signal.

Agent-based systems have been widely used to develop industrial control systems when they are required to address issues such as flexibility, scalability and portability. The most common approach to develop such control systems is with agents embedded in a platform that provides software libraries and runtime services that ease the development process. These platforms also bring challenges to the agent-based control system engineering. For example, they might introduce default design features, such as a global directory of agents. Furthermore, agents are generally locked in a platform and depend on the platform's available support for deployment across computing infrastructures. This article addresses these challenges through an approach for building agent-based control systems, that relaxes the dependencies in multiagent system (MAS) platforms, through the use of container-based virtualisation. The proposed approach is elaborated via a reference architecture that enables the implementation of agents as self-contained applications that can be deployed, on-demand, in independent environments but still are able to communicate and coordinate with other agents of the control system. We built a prototype using this approach and evaluated it in the context of a case study for the supervisory control of digital network infrastructures. This case study enabled us to demonstrate feasibility of the approach and to show the flexibility, of the resulting control system, to adopt several topologies as well as to operate at different scales, over emulated networks. We also concluded that designing agents as individual deployment units is also cost-effective especially in control scenarios with low number of stable agents.

Systems engineering crosses multiple engineering disciplines for the design, control, and overall management of engineered systems