Techniques in Operations Research (MAST30013)
Undergraduate level 3Points: 12.5On Campus (Parkville)
Overview
Availability | Semester 1 |
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Fees | Look up fees |
Operations Research aims to develop and apply scientific methods to improve decision-making. It deals with theoretical development of models and methods, as well as their practical applications to find optimal or near-optimal solutions to problems arising in various disciplines, such as engineering, management, economics, and education.
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.
Intended 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;
Generic skills
On completion of this subject students should have developed the following generic skills:
- 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.
Last updated: 14 March 2025