Available in 2017

Course handbook


Often known as the 'science of better', Operations Research (OR) is the cornerstone of effective decision making. OR has enhanced organisations 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 is intended for students looking to advance their fundamental knowledge of OR techniques as seen in MATH2730. The course delves into both practical and theoretical aspects of established OR techniques and results. The course will better equip students to judge the applicability of methods for given problems, and answer questions as to why two seemingly similar problems differ dramatically with respect to tractability. By the end of the course students will be able to apply known OR techniques effectively and also, develop their own solutions for newer, more challenging problems. Throughout, use will be made of relevant and widely used software packages.

Availability2017 Course Timetables


  • Semester 2 - 2017

Learning outcomes

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

1. A thorough understanding of the quantitative techniques and theory behind Operations Research.

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

3. An advanced repertoire of problem-solving skills that is both analytical and flexible.


The course will cover two main areas:1.  Nonlinear programming, including

  • a.  necessary and sufficient conditions for optimality,
  • b.  solution methods,
  • c.  numerical solution; and

2.  Probability models in operations research, with topics selected from

  • a.  queueing theory,
  • b.  revenue management,
  • c.  Markov decision processes, and
  • d.  inventory control.

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

Assumed knowledge

Any 2000 level mathematics course.

Assessment items

Written Assignment: Assignment (x4)

Formal Examination: Final Examination

In Term Test: Mid-Semester Exam

Contact hours



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

Tutorial and computer laboratory work will be integrated with lecture material as required.