School of Computer Science

MSc Multidisciplinary Optimisation - 2013

Final Award MSc
Interim Award PGCert/PGDip
Programme TitleMultidisciplinary Optimisation
School/DepartmentSchool of Computer Science
Banner Code9150
Length of Programme1 years
Total Credits180
UCAS CodeNone
Awarding InstitutionThe University of Birmingham
QAA Benchmarking GroupsComputing

Educational Aims Of Programme

  • This distinct programme covers the field of optimisation from a highly multi-disciplinary point of view. It includes mathematical programming methods, heuristic optimisation as well as metaheuristic optimisation. It treats optimisation holistically and provides the students with a unique set of skills that neither computer science nor mathematics could provide easily.
  • Some (not all!) examples of the topics include linear and nonlinear programming, mixed integer programming, conic programming, heuristic optimisation, meta-heuristic optimisation (evolutionary optimisation, ant colony optimisation, tabu search, simulated annealing, ...), constraint handling, multi-objective optimisation, dynamic optimisation, machine learning, data analysis, etc.
  • Not only will students learn about the technical knowledge, they will also get opportunities to apply them and gain first-hand experiences through project work. They will have opportunities to apply what they have learned to solve problems in different fields, and this will allow them to specialise according to their strength and interest.
  • The programme emphasises transferable skills and has incorporated a research skills module. It will also reinforce such skills through project work. We expect the students either to move into industry or to continue their postgraduate studies (towards PhD) after they have completed this degree.

Programme Outcomes and Learning, Teaching and Assessment Strategies

Knowledge and Understanding

  • Mathematical programming methods.
  • Heuristic optimisation methods.
  • Meta-heuristic optimisation algorithms

Skills & Other Attributes

  • To formulate a complex optimisation problem.

Transferable Skills

  • To solve optimisation problems using appropriate methods.
  • To communicate technical and mathematical material clearly.


  1. The Learning & Teaching and Assessment Methods above are not intended to be exclusive, but to indicate the main methods in use. Module Descriptions contain more detail.
  2. It is not intended to admit students to PGCert and PGDip programmes, although these qualifications will be available to students who have met the minimum requirements given in University Regulations.