Car production line

Management scienceOptimisation & analytics

Every real world system requires many decisions to be made, such as:

  • which facilities to operate and when
  • how much to manufacture of a particular product
  • which routes to use for each vehicle of a fleet

Many constraints exist due to physical, legal or contractual limits, such as available capacities or budgets, union agreements or government regulations. Every real world system also generates a wealth of data with many facets, presenting unlimited opportunities for better understanding of such systems.

What is optimisation & analytics about?

Analytics, broadly speaking, extracts knowledge from data, to discover, interpret and communicate past behaviours, and to enable data-driven decision-making.  Analytics is split into three major areas:

  1. Descriptive analytics primarily concerns with establishing an understanding of existing systems.
  2. Predictive analytics aims to make more accurate predictions about the short- and long-term future.
  3. Prescriptive analytics supports decision makers to achieve the best decisions within system constraints, which is primarily achieved through optimisation techniques.  

Optimisation and analytics comprise a rich set of tools which can be used to support informed decision making.

The optimisation & analytics research group

The optimisation & analytics group in the department of Management Science is interested in developing theory, solution methods and algorithms for challenging optimisation and predictive analytics problems stemming from various real-world applications.

The group consists of five full-time academic staff. We are actively working on project with many sectors, including transportation and logistics, health, manufacturing, energy and local/national governments.

Current projects

PhD opportunities

The group will welcome any PhD proposals relevant to their research interests and expertise, and would also offer the following potential topics to any candidate interested:

Dr. Ashwin Arulselvan

  • MIP formulations and cutting plane techniques for evacuation modelling
  • Design of robust telecommunication networks
  • Data mining in social and biological networks
  • Bilevel integer programming problems

Dr. Kerem Akartunali

  • Network design for healthcare distribution systems
  • Robust radiation treatment planning optimisation
  • Extended formulations and valid inequalities for lot-sizing in remanufacturing
  • Designing a sustainable energy network using renewables

Dr. Mahdi Doostmohammadi

  • MIP for production planning (manufacturing and remanufacturing) problems
  • Robust production planning problems
  • MIP for (inventory) routing problems
  • Bilevel/biobjective mixed integer programming problems
  • Network design for telecommunications, healthcare and energy systems

Find out more about how to apply for a PhD in Management Science.