The Statistics & Operational Research Group (SORG) unites research and teaching staff from the Department of Mathematical Sciences of the School of Computing & Mathematical Sciences.

The group's capabilities in research and consultancy involve:

  • General operational research problems.
  • Scheduling problems in manufacturing and service.
  • Environmental statistics and stochastic modelling.

The Statistics & Operational Research Group specialises in:

  • Methodological aspects of operational research with a focus on the development of models and algorithms for problems of combinatorial optimisation (integer programming, deterministic machine scheduling, submodular optimisation).
  • Stochastic point process modelling with likelihood approach for environmental applications, particularly in hydrology.
  • Mathematics and statistics education.

Areas of research

Scheduling and combinatorial optimisation

Research in this area is related to the studies of methodological aspects of scheduling and includes the development of exact and approximation algorithms and schemes for enhanced scheduling models that contain additional logistics components; for example, changing and controllable processing times, machine maintenance and non-availability intervals, interstage transportation, introducing additional processing machines, estimating the power of preemption, etc. The history of scheduling is another area of interest, and has generated several publications.

Since 2008, more than 30 peer-reviewed papers have been published on these topics by SORG members.

Novel techniques that allow the creation of general frameworks of handling a wide range of scheduling models are:

  • Quadratic Boolean programming problems
  • Reformulation of scheduling problems with controllable processing parameters in terms of linear programming problems with submodular constraints.

Stochastic modelling

This research direction belongs to the area of stochastic modelling with a likelihood approach to environmental applications. A key area of interest is to develop stochastic point process models, which make use of likelihood modelling techniques, for fine-scale rainfall using a class of Cox processes. In this line of work, SORG members are collaborating with colleagues from a world renowned research group in this field at Imperial College London, on modelling rainfall time series collected over multiple stations in a catchment area, making use of local covariate information.

Another area of interest is the development of collaborative work with colleagues at Imperial College London on regional rainfall modelling using a class of hidden Markov models with additional dependence.

Maths education

The Statistics & Operational Research Group has been researching innovative approaches to enhance mathematics and statistics education, student experience and performance in higher education, as well as student employability. Results of this work have been presented regularly at annual MSOR-CETL conferences and the Higher Education Academy (HEA) STEM conferences over the last five years.

There has been an increased effort and activity in introducing innovative methods in teaching and learning, and also shaping up the existing ones, across the country in the recent past. This line of work and the findings of the research group's work in the area are expected to contribute and impact positively on mathematics and statistics education while providing students with enhanced learning experiences.

Recently, the group has been working on an employability project funded by a teaching development grant from the HEA, with Dr Ramesh as the principal investigator. The project is aimed at enhancing our undergraduate students' employability skills. This 15-month long project (2012-13) has been running very successfully and many students have benefitted from the activities organised under this project.

Members of the group also take active roles in national events and conferences organised by the HEA and the MSOR network.

Recent PhD completions

Kabir Rustogi

PhD thesis, 2013

Machine scheduling with changing processing times and rate-modifying activities. Awarded the Best PhD Thesis Prize of the Operational Research Society. Supervisors: Professor Strusevich and Dr Ramesh.

Richard Quibell

MRes thesis, 2012

Heuristics algorithms for scheduling jobs in a partially ordered, three-machine environment. Supervisor: Professor Strusevich.

Contact us

Professor Vitaly Strusevich

Telephone: +44 (0)20 8331 8662

Dr Nadarajah Ramesh

Telephone: +44(0)20 8331 8734.