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:
- Descriptive analytics primarily concerns with establishing an understanding of existing systems.
- Predictive analytics aims to make more accurate predictions about the short- and long-term future.
- 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.
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