Decision and Cognitive Sciences Research Centre

Research themes

Research themes

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

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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.


  • Case study 1: A data-driven framework for efficient bidding of product advertisement. This case study describes a collaborative project between UoM and a leading Digital Marketing company specialized in bidding for product advertisement. The objective of the project was to design, develop and embed a new generation algorithms which constantly use historical data to learn and adapt online bidding strategies according to the dynamics of the market and human behavior. The new generation algorithm has continuous learning capabilities and makes sophisticated and educated recommendations whilst satisfying client’s revenue goals and profits, and minimizing the need for manual intervention of mathematicians to sanitize the recommendations.

    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

  • Case study 1: Data-driven design an optimization of biopharmaceutical manufacturing processes. This case study describes a collaborative project between UoM and a consultancy company specialised in modelling drug manufacturing processes. The objective of project was to extend an existing simulation model for manufacturing processes with a flexible data-driven optimization tool capable of creating autonomously manufacturing processes that meet user-specified objectives in terms of, for example, cost-efficiency, process robustness, environmental impact, and facility footprint. The project is funded by Innovate UK and the outcome so far is a prototype software that is currently being tested by various clients in the pharma sector. Commercialisation of the new simulation-optimization tool will commence following testing.

    Project Lead: Richard Allmendinger

    Amount of money invested in the case study/project to date: £180K

    Project duration: 24 months

    Domain of application: Pharma

    Keywords: Bayesian optimization, automated decision making, digitising manufacturing systems, uncertainty, multiple objectives

  • Case study 2: Optimisation of transport flow and resource utilisation across healthcare. A partnership between a local healthcare company and the Decision and Cognitive Sciences Research Centre of Alliance Manchester Business School to develop new tools to improve transport flow and resource utilisation across health and care to improve quality of care for service users and efficiency for providers. This project is funded by InnovateUK and has recently started.

    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.

  • Case study 1: Intelligent data driven and knowledge-based fraud prevention and detection. Alliance MBS has been successful in a KTP (Knowledge Transfer Partnership) bid with law firm Kennedys to help tackle insurance fraud. The two-year project, co-financed by Innovate UK, will develop and embed an intelligent data driven and knowledge-based fraud prevention and detection service to support insurance claim handling. It will use modern machine learning, data analytics techniques, semantic technologies, intelligent modelling methodologies and decision support systems. The academic team supporting the two-year project were Jian-Bo Yang, Professor of Decision and System Sciences and Director of the Decision and Cognitive Sciences Research Centre (DCSRC), and Dong Ling Xu, Professor of Decision Science and Support Systems. Explained Prof Yang: “Fraud is a major enemy of insurance companies, with huge costs not just to the industry but also to consumers via increased premiums. At the moment the industry tries to prevent fraud by looking for and detecting suspicious patterns but criminals are very clever and always trying to stay one step ahead, so attempting to identify new patterns as soon as they arise is key.”

    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.