Course Title: Advanced Optimisation with Python

Part A: Course Overview

Course Title: Advanced Optimisation with Python

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2015


City Campus


171H School of Science


Sem 2 2020

Course Coordinator: Prof. John Hearne

Course Coordinator Phone: +61 3 9925 9046

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1293 Mathematical Modelling and Decision Analysis

Course Description

Optimisation models are amongst the most widely used models in operations research and management science. They are used to solve a diverse range of problems comprising telecommunications, transport, timetabling, scheduling, workforce planning, loading, cutting and more. This course concentrates on formulating and building such models, solving them using commercial software and interpreting their solution.  Some understanding of the methods useful for solving optimisation problems will also be covered.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical knowledge, methodologies and modern computational tools.


  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the relationship between the purpose of a model and the appropriate level of complexity and accuracy.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

On completion of this course you should be able to:

  1. Utilise power of mathematical programming and its range of applications;
  2. Formulate and solve mixed integer programming (MIP) models;
  3. Implement MIP models using commercial software;
  4. Use heuristic optimisation methods to solve problems;
  5. Interpret the solution to MIP models.

Overview of Learning Activities

The outcomes for this course are best achieved through hands-on experience. Introductory lectures will cover some basic mathematical techniques required to formulate mixed integer programming problem. An introduction to the principles behind solution methods will be presented and how this should influence formulation. An illustration will be presented on how to solve an MIP model using a commercial software package. After that numerous real problems will be presented. These problems will be distributed amongst small groups for their analysis and subsequent report back to the class. This method allows a wide range of problems to be covered while also giving everyone practical problem-solving experience. Some individual assignments will also form part of the course.

Overview of Learning Resources

A recommended reading list will be provided. Use will also be made of several online sources of material. Required software packages will be available in the computer laboratory. It will be possible to undertake most of the projects using free software for home use.

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

Assessment Tasks:

Early Assessment Task:  Online Tests

Weighting 10%

This assessment task supports CLOs 1, 2

Assessment Task 2: Projects

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4, and 5

Assessment Task 3: Lab Test

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4, and 5


Assessment 4: Final Exam

Weighting 50% 

This assessment task supports CLOs 1, 2, 3, 4, and 5