UCL Centre for Systems Engineering


Systems Modelling and Optimisation

System dynamic modelling, Cost-benefit analysis, Soft Systems Methodology, Intelligent systems, Adaptive modelling

A systems view of the world can be applied to good effect in most projects.

We have applied one systems thinking technique ('soft systems methodology') to help an aerospace prime contractor to learn more from its experience in previous projects. We have also used this to understand the process of discharging patients from an acute hospital.

We applied another technique ('system dynamics') to think about the implications of introducing a variable charge for household waste collection on the incidence of fly-tipping.

This is an example of a joint project we have carried out with UCL's Jill Dando Institute of Crime Science - applying systems techniques to understand the causes of crime.

We are experts in systems modelling, and are working with BAE Systems and Alexander Dennis buses on a £4m project to develop hybrid electric/diesel/fuel cell technology for passenger transit buses. The project - called Hybrid Electric Technology for Transit Buses - will last three years, and is being funded by the Government's Technology Strategy Board and the Department for Transport.

UCL's role is to perform a cost-benefit analysis of hybrid technologies relative to traditional technologies, to develop a model to optimise the energy storage and power management throughout the vehicle, and to predict the challenges of integrating the technology into the existing system.

Our Research Projects

Thesis: Robotic cites modelling methodologies for interdependent infrastructures and sociotechnical systems resilience.


Yosoph Sindi


This research is based on various century’s challenges of mass migration towards urban cities.
Many governments are heading towards the concepts of smart cities, smart ports and smart
governments as one form of coping with these challenges. The research will first demonstrate that such cities, contain inherently socio‐technically wicked problems. This research presents how important it is to take these problems into consideration when modelling such complex systems (considering minimum level of complexity). Another factor is that the socio-technical elements and cities infrastructures are interdependent. Hard systems of smart city grids and soft-systems related to services are progressing towards more autonomy requiring a new kind of intelligence. This is in need of resilience planning and with a degree of global interconnectivity. 
Due to the progress towards automation and autonomy Internet of things (IoT) is progressing towards Internet of Robotic things (IoRT). A Robotic city consists of the automated and interdependent smart-city infrastructure formed as a distributed robotic systems (mostly heterogeneous). This challenge differs from conventional robotics systems due to the involvement of socio‐technical systems, policy, complex circular lifecycle targets, IoT/IoRT, the mixed bottom-up and top-to-bottom cases. It also differs from smart city challenge due to the level of autonomy, IoRT, and socio‐technical agility. 
The proposed study methodology is to analyse sample problems that satisfy minimum level of
system complexity in a limited sample geographic area (satisfying what is commonly known as Compact Urban Centre CUC). Systems construction will be co-created with groups of stakeholders selected by the research industrial partners. The research will also investigate the difficulties in relation to urban resilience and robotic cities networks as part of a global system. 
The research modelling approach is to consider the smart city infrastructure as a heterogeneous distributed robotics system to be modelled as the individual agents in the system. The heterogeneous agents will be influenced by the inhabitants where the influence on each agent is a result or outcomes from a 4D Cellular Automata model that emulates the citizens behaviour. 
The conclusion of this research presents the effectiveness, scalability and agility of a new way to approaching such critical and complex systems. 


Thesis: Application of systems dynamics techniques to understand and improve rework across complex projects


Juan Carlos Guerrero Andrade


The size of the global space market was around £ 250 billion in 2014 [1]. As in other sectors where complex and technologically challenging projects are delivered, organizations and companies within the space sector continue to have problems regarding cost and schedule performance. For example, projects at NASA have an average of 27.6% cost overrun and 13 months delay relative to the original schedule [2]. According to Parchami [3], labour and consultant workforce rework were among the most critical factors influencing delay.

Although there are various specific definitions of rework depending on the industry under investigation, rework is generally understood to include the task or activities that must be done to fix defects, quality deviations, failings in functions or performance to comply with the original user requirements. This research is carried out with the Department of Space and Climate Physics at UCL, which has a strong motivation to understand the rework cycle as well as the different types of archetypes that affect space projects.

Systems dynamics is the principal method used to research rework in space projects. This is based on the use of graphical notation incorporating both qualitative and quantitative data (with the use of causal loop models and stock and flow models respectively) and is applied by designing the qualitative factors inside the scope of the study through literature review and surveys to experts in the field and elaborate diagrams of causality to comprehend in-depth the variables and the causes that affect rework. The quantitative data is then examined with simulation models that capture the complex behaviour of the systems, and results are gathered in a different graphical pattern allowing identification of systematic problems with the use of computer simulation models. Policies for improving the system or project are modelled to understand the potential benefits in a test environment; this ultimately enables a better understanding and management of problems.

[1]            United States Space Foundation., The space report 2014 : the authoritative guide to global space activity. 2014.

[2]            Government Accountability Office, “Assessments of Major Projects,” 2019.

[3]            M. Parchami Jalal and S. Shoar, “A hybrid SD-DEMATEL approach to develop a delay model for construction projects,” Eng. Constr. Archit. Manag., vol. 24, no. 4, pp. 629–651, Jul. 2017.