Whilst it is neither possible nor desirable to be exclusive about specific research themes, four research themes have been identified, each with a dedicated theme leader(s). The research themes will form distinctive yet inter-linked and synergistic focus points for organising, promoting and conducting research to achieve the DCS objectives. The theme leaders will be proactive and take leadership in developing and coordinating research in their theme areas. They will be a first point of contact for research collaboration and project development in their areas of interest. The four research themes and theme leaders are listed in the following table.
Theme leader: Nadia Papamichail / Yu-Wang Chen, Oscar De Bruijn, Manuel Lopez-Ibanez, Dong-Ling, Jian-Bo Yang, Xiao-Jun Zeng
to be updated
Theme leader: Julia Handl / Richard Allmendinger, Jian-Bo Yang, Dong-Ling Xu, Swati Sachan, Xiao-Jun Zeng, John Keane
The center has a track record of research in the area of data analytics and its applications. We have specific expertise in the areas of probabilistic inference, itemset mining, text mining, fuzzy systems and multicriterion approaches to data analysis. Novel tools developed in our center include the Evidential Reasoning (ER) Approach, the Belief-Rule-Base (BRB) Approach, the multiobjective clustering method MOCK and PriEsT an interactive decision support tool to estimate priorities from Analytic Hierarchy Process (AHP)-based pairwise comparison judgments.
Academics within the theme develop customized approaches suitable for applications ranging from Energy, Finance, Operations, Pricing, Product Development and Marketing to Bioengineering and Healthcare. Typically, these applications involve the integration of complex (and often big) data sources, the use of explorative methods to obtain insight into the structure and relationships in the data, or the development of predictive models that can support particular business needs. Current Knowledge Transfer Partnerships with industry include projects with Dream Agility, 365 Response and Kennedys Law.
Project Lead: Richard Allmendinger
Amount of money invested in the case study/project to date: £180K
Project duration: 24 months
Domain of application: Digital Marketing
Keywords: Multiobjective optimization, multiobjective clustering, dimensionality reduction, feature engineering, game theory, artificial neural networks
Theme leader: Richard Allmendinger / Julia Handl, Jian-Bo Yang, Manuel Lopez-Ibanez
The center has extensive experience in the area of modeling, simulation and optimisation and the application of these concepts to real-world problems. We have specific expertise in the areas of heuristics, evolutionary computation, automatic configuration of optimization algorithms, multiobjective optimization, multi-criteria decision-making, and optimization subject to expensive evaluations and uncertainty. Most of our work is interdisciplinary and often in collaboration with industrial partners.
Academics within the theme develop and apply novel optimization techniques to a variety of problems arising, for example, in Healthcare, Manufacturing, Software and Product Design, Marketing, and Portfolio Optimization. Furthermore, the academics contribute also to the design of mathematical models (e.g. a manufacturing process or behavior of decision makers) and the translation of these models into a (computational) simulator or an experimental platform, which then interacts with an optimization algorithm.
Keywords: Heuristic Methods, Evolutionary Algorithms, Multiobjective Optimization, Bayesian Optimization, Closed-Loop Optimization, Data-Driven Optimization, Model Development, Real-World Problems, Simulator Design
Previous Partners include: ARM, Dream Agility, MedImmune, RepliGen, Allergan
Domain of application: Pharma
Keywords: Bayesian optimization, automated decision making, digitising manufacturing systems, uncertainty, multiple objectives
Project Lead: Manuel López-Ibáñez
Amount of money invested in the case study/project to date: £240K
Project duration: 30 months
Domain of application: Transport in healthcare
Keywords: Optimisation, simulation, machine learning, transportation, planning, management, uncertainty, prediction, healthcare
Theme leader: Yu-Wang Chen / Jian-Bo Yang, Dong-Ling Xu, Swati Sachan, Nadia Papamichail
In the DCSRC, we have an established research strength in the areas of artificial intelligence and knowledge-based systems. Specific work involves intelligent fraud prevention, predictive analytics, customer analytics, metaheuristics, belief rule-based systems and intelligent decision support systems (IDSS). For example, academics within the theme are currently working with Forensic Testing Service to develop an automated data analytics tool that analyses all of the available information and then makes informative and explainable recommendations for drug and alcohol testing cases. An intelligent machine learning based system has been built to accurately predict the prices of used motorcycles for an online motorcycle valuation comparison site.
Our research on IDSS focuses primarily on theoretical methods for multiple-criteria decision-making and applications of intelligent technologies, such as expert systems, evidential reasoning, fuzzy logic, belief rule-based models and genetic algorithms. A software tool, called Intelligent Decision System (IDS) has been developed for solving many decision problems, such as clinical decision support, engineering system fault diagnosis, portfolio optimisation, performance modelling and impact assessment of sustainable energy systems. The system is also used by practitioners, decision analysts and researchers from over 30 countries including organisations such as General Motors Company, Belgian Nuclear Research Centre, and Hong Kong Productivity Council. Current research collaboration with industry include projects with Forensic Testing Service and Kennedys Law.
Project Lead: Jian-bo Yang
The DCS management team consists of the Directors and Coordinators who are in charge of the day to day running of DCS. The management team and the theme leaders form the DCS management committee, which meets to set the policies and directions for DCS. The theme leaders are responsible for organising project meetings to initiate new research projects and manage existing research projects as appropriate.