OR428      Half Unit
Model Building in Mathematical Programming

This information is for the 2013/14 session.

Teacher responsible

Dr Katharina Schwaiger


This course is compulsory on the MSc in Management Science (Operational Research). This course is available on the MSc in Applicable Mathematics. This course is available as an outside option to students on other programmes where regulations permit.


Students must have  a knowledge of Mathematics and Statistics to the level of MA107 (Quantitative Methods - Mathematics) and ST102 (Elementary Statistical Theory).

Course content

Mathematical Programming is one of the most powerful and widely-used quantitative techniques for making optimal decisions. The course has a pragmatic focus and aims at enabling students to model and solve real-life management problems. In providing an overview of the most relevant techniques of the field, it teaches a range of approaches to building Mathematical Programming models and shows how to solve them and analyse their solutions. Content includes: An introduction to the theory of linear programming. The modelling life cycle and modelling environments. Formulation of management problems using linear and network models; solution of such problems with a special-purpose programming language; interpretation of the solutions; limitations of such models. Formulation and solution of non-linear models including some or all of binary, integer, convex and stochastic programming models


20 hours of lectures, 20 hours of seminars and 9 hours of computer workshops in the MT. 3 hours of computer workshops in the LT.

Computer Workshops are optional.

Formative coursework

Students will have the opportunity to submit a mock project for marking and comment before the final assessed project is due.

Indicative reading

Central to the course is: H P Williams, Model Building in Mathematical Programming, Wiley. A good introduction to Mathematical Programming provide the relevant chapters of: F S Hillier and G J Liebermann: Introduction to Operations Research, McGraw-Hill. A more theoretical treatment can be found in: D Bertsimas and J N Tsitsiklis: Introduction to Linear Optimization, Athena Scientific. Further suggestions for reading are given during the course.


Continuous assessment (100%) in the MT.

This course is examined entirely by weekly exercises and/or by project. Written work is marked on presentation as well as on content.

Key facts

Department: Management Science Group

Total students 2012/13: 57

Average class size 2012/13: 19

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Team working
  • Problem solving
  • Application of numeracy skills
  • Specialist skills