Introductory description

The module aims to develop the learner’s interest in, and knowledge and understanding of, various optimisation models to support decision making in organisations.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Optimisation modelling: mathematical programming, including extensive studies of linear programming and dynamic programming; integer programming, introduction to non-linear optimisation, introduction to algorithms and heuristics. The techniques mentioned are illustrated on a range of real life applications. IT tools (Excel Solver, Python libraries, etc.) are used to demonstrate the usage of the theory in practice.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate knowledge of Operational Research techniques and effective problem solving and decision making skills.
• Ability to identify problem structures, to suggest solution approaches and to identify potential quality issues of the solution.

Indicative reading list

K.R. Baker (2006): Optimization Modeling with Spreadsheets, Thomson/Brooks/Cole.
C. Albright and W. Winston (2011), Management Science Modeling, Thomson/South-Western.
W. Winston, Operations Research: Applications and Algorithms, Any edition.

Subject specific skills

Given a practical problem, a student should be able to:

1. Formulate a model;
2. Select the most efficient method to tackle the problem;
3. Use appropriate software to solve the model;
4. Report on the findings using a range of media which are widely used in business
5. Reflect on the validity of the model;
6. Reflect on the modelling process accepted.

Transferable skills

Discussion and analysis of case material develops oral communication skills.
Numeracy skills.
Effective teamwork in case based discussion and presentations.