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Optimisation for Industry (MAST90014)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Email: alysson.costa@unimelb.edu.au
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:
- have learned how basic techniques in operations research are applied in industry;
- understand how to turn an industrial problem into a mathematical formulation;
- know how to solve important mathematical optimisation problems arising in industrial framework;
- gain the ability to pursue further studies in this and related areas.
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;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10007 | Linear Algebra |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
|
12.5 |
OR:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10008 | Accelerated Mathematics 1 | Semester 1 (On Campus - Parkville) |
12.5 |
or equivalent.
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: 3 November 2022
Assessment
Additional details
One written assignment (20%, due mid semester), one group project (40% including one group report and one group presentation, due late in semester), a two-hour written examination (40%, in the examination period).
Last updated: 3 November 2022
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 4 March 2019 to 2 June 2019 Last self-enrol date 15 March 2019 Census date 31 March 2019 Last date to withdraw without fail 10 May 2019 Assessment period ends 28 June 2019 Semester 1 contact information
Email: alysson.costa@unimelb.edu.au
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
None
Recommended texts and other resources
Optimization Over Integers,
By Dimitris Bertsimas and Robert Weismantel
Integer and Combinatorial Optimization
by Laurence A. Wolsey, George L. Nemhauser
Integer Programming
by Laurence A. Wolsey
Julia Programming for Operations Research: A primer on computing,
By Changhyun Kwon
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Data Science Course Ph.D.- Engineering Course Master of Energy Systems Informal specialisation Mathematics and Statistics Specialisation (formal) Mechatronics - 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.
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
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022