The University of Newcastle, Australia
Available in 2018

Course handbook


Often known as the 'science of better'; Operations Research (OR) is the cornerstone of effective decision making. OR has enhanced organizations and experiences all around us. From more cost effective scheduling of airline crews to the design of less intrusive cancer radiation therapy treatments, from two-person start-ups to Fortune 500 companies, from global resource planning decisions to optimizing local postal delivery routes, all benefit directly from OR.

This course introduces students to the fundamental problems in OR and the essential mathematical modelling theory and techniques needed to make more effective decisions and build more productive systems. As well as surveying general techniques, the subject will focus on a number of illustrative case studies. Throughout, use will be made of relevant and widely used software packages.

Availability2018 Course Timetables


  • Semester 1 - 2018

Learning outcomes

On successful completion of the course students will be able to:

1. A basic understanding of quantitative techniques used to help in the management of business, industrial and organisational operations and projects.

2. An appreciation of the use of a mathematical optimisation model as a way of encapsulating and analysing a complex situation.

3. Developed an approach to problem-solving that is both analytical and flexible.


The course will cover three main areas:

  1. Linear programming, including sensitivity analysis and an introduction to duality;
  2. Network optimisation; and
  3. Integer programming.

For each topic, applications, modelling, and methods will be explored. Appropriate software packages for modelling and solutions of problems will be covered.

Assumed knowledge

At least one of the following courses: MATH1110, MATH1210, MATH1510, ECON1003, STAT1060, STAT1070.

Assessment items

Written Assignment: Individual Written Assignment

Project: Computer Project

Formal Examination: Examination

Contact hours


Computer Lab

Face to Face On Campus 1 hour(s) per Week for Full Term


Face to Face On Campus 3 hour(s) per Week for Full Term