Introductory description

This module aims to provide doctoral students with skills and knowledge to model and formally define decision and optimisation problems and apply a range of methods to solve these problems.

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.

Introduction to programming in Python or Matlab
Design of computer experiments
Linear Optimisation
Integer programming
Global Optimisation
Multi-objective optimisation and decision making
Stochastic modelling of optimisation problems
Simulation

Learning outcomes

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

• An in-depth knowledge of different types of optimisation and decision problems, and their respective challenges
• An in-depth understanding of assumptions underlying various optimisation and modelling approaches
• An in-depth knowledge of analytics tools commonly used in operational research

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

An in-depth knowledge of different types of optimisation and decision problems, and their respective challenges
An in-depth understanding of assumptions underlying various optimisation and modelling approaches
An in-depth knowledge of analytics tools commonly used in operational research
Model and formally define a decision and optimisation problem
Understand the different categories of optimisation problems
Understand the strengths and limitations of different algorithmic approaches
Use a variety of methods to solve specific optimisation problems

Transferable skills

Problem solving abilities.
Modelling skills.
Analytical skills.
Programming skills.
Confidence as user of optimisation software