Optimisation for Industry (MAST90014)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
The use of mathematical optimisation is widespread in business, where it is a key analytical tool for managing and planning business operations. It is also required in many industrial processes and is useful to government and community organizations. This subject will expose students to operations research techniques as used in industry. A heavy emphasis will be placed on the modelling process that turns an industrial problem into a mathematical formulation. The focus will then be on how to solve the resulting mathematical problem with mixed-integer programming techniques.
Intended learning outcomes
After completing this subject students should be able to:
- Demonstrate a critical understanding of Operations Research techniques and their applications in industry and public decision-making;
- Formulate mathematical optimisation models for industrial decision-making problems;
- Identify appropriate solution methods for mixed-integer programming models;
- Solve optimisation problems arising in industrial frameworks using Python and appropriate black-box solvers.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they 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; and
- Time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST10007 | Linear Algebra |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
|
12.5 |
| MAST10008 | Accelerated Mathematics 1 | Semester 1 (On Campus - Parkville) |
12.5 |
| MAST10022 | Linear Algebra: Advanced | Semester 1 (On Campus - Parkville) |
12.5 |
OR
Admission into the MC-SCIMAT Master of Science (Mathematics and Statistics)
OR
Admission into the MC-DATASC Master of Data Science
OR
Admission into the MC-ELECENG Master of Electrical Engineering
OR
Admission into the MC-INDENG Master of Industrial Engineering
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
It is recommended that students have completed a third year subject in linear and non-linear programming equivalent to MAST30013 Techniques in Operations Research or MAST30022 Decision Making.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
A written assignment including a computational implementation of an industrial optimisation model
| Mid semester | 20% |
One group project: consisting of six students to a group including one group written report and one 10-minute group presentation, due late in semester. Each student should contribute 40 hours of work towards the report and presentation.
| Second half of the teaching period | 40% |
A written examination
| During the examination period | 40% |
Last updated: 4 March 2025
Dates & times
- Semester 1
Principal coordinator Alysson Machado Costa Mode of delivery On Campus (Parkville) Contact hours 36 hours comprising one 2-hour lecture per week and one 1-hour computer lab/practical class per week. Total time commitment 170 hours Teaching period 3 March 2025 to 1 June 2025 Last self-enrol date 14 March 2025 Census date 31 March 2025 Last date to withdraw without fail 9 May 2025 Assessment period ends 27 June 2025 Semester 1 contact information
Email: alysson.costa@unimelb.edu.au
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Mathematics and Statistics) Course Master of Data Science Course Master of Energy Systems Specialisation (formal) Mechatronics Informal specialisation Mathematics and Statistics - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 4 March 2025