MAST30013 Techniques in Operations Research

Credit Points: 12.5
Level: 3 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2017:

Semester 1, Parkville - Taught on campus.Show/hide details
Pre-teaching Period Start not applicable
Teaching Period 27-Feb-2017 to 28-May-2017
Assessment Period End 23-Jun-2017
Last date to Self-Enrol 10-Mar-2017
Census Date 31-Mar-2017
Last date to Withdraw without fail 05-May-2017

Timetable can be viewed here.
For information about these dates, click here.
Time Commitment: Contact Hours: 3 x one hour lectures per week, 1 x one hour practice class per week
Total Time Commitment:

Estimated total time commitment of 170 hours

Study Period Commencement:
Credit Points:
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability


Dr Alysson Machado Costa


Email: alysson.costa@unimelb.edu.au

Subject Overview:

This subject introduces some major techniques and algorithms for solving nonlinear optimisation problems. Unconstrained and constrained systems will be considered, for both convex and non-convex problems. The methods covered include: interval search techniques, Newton and quasi-Newton methods, penalty methods for nonlinear programs, and methods based on duality. The emphasis is both on being able to apply and implement the techniques discussed, and on understanding the underlying mathematical principles. Examples involve the formulation of operations research models for linear regression, multi-facility location analysis and network flow optimisation.

A significant part of the subject is the project, where students work in groups on a practical operations research problem.

Learning Outcomes:

On completion of this subject students should develop

  • skills in setting up operations research models;
  • a knowledge of the most important techniques for solving nonlinear optimisation problems;
  • an understanding of the role of algorithmic thinking in the solution of operations research problems;
  • competence in the use of computer packages in operations research;
  • an understanding of the factors and restrictions involved in building and using models for planning and management problems.

Three written assignments due at regular intervals during semester amounting to a total of up to 50 pages (30%), a group project involving a written report of up to 20 pages due at the end of semester (15%) and a 15-minute oral presentation at the end of semester (5%), and a 2-hour written examination in the examination period (50%).

Prescribed Texts:


Recommended Texts:

H. A. Taha, Operations Research: An Introduction, McMillan, 5th Ed,1992.

W. L. Winston, Operations Research: Applications and Algorithms, PWS-Kent, 1987.

R. Fletcher, Practical Methods of Optimization, 2nd Ed, John Wiley & Sons, NY, 1987.

Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

In addition to learning specific skills that will assist in their future careers in science, students will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments;
  • computer skills: the ability to use mathematical computing packages.

This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.

Related Majors/Minors/Specialisations: Discrete Mathematics / Operations Research
Discrete Mathematics / Operations Research
Discrete Mathematics / Operations Research
Discrete Mathematics / Operations Research
Discrete Mathematics and Operations Research (specialisation of Mathematics and Statistics major)
Science-credited subjects - new generation B-SCI and B-ENG.
Selective subjects for B-BMED

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