FIT5216 Modelling discrete optimisation problems
Faculty of Information Technology
FIT5216 Modelling discrete optimisation problems is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2024 in Semester 1 at Clayton, Malaysia and Suzhou (SEU). It needs FIT9133, FIT9136 or FIT9131.
- Credit points
- 6
- Offered in 2024
- Semester 1
- Clayton, Malaysia, Suzhou (SEU)
- Assessment
- Exam 40%
- and 5 other tasks
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
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Requisites
Before FIT5216
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After FIT5216
No unit lists FIT5216 as a prerequisite in the 2024 handbook.
Enrolment rules
Prerequisite: For students enrolled in E3001, E3002, E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2099
Prerequisite: For C6007 students who commended in 2020: None
Overview
This unit introduces the fundamentals of modelling for discrete optimisation, focusing on how to rigorously express a discrete optimisation problem in a manner that is can be solved. Topics covered will include decision variables, basic constraints, modelling with sets, modelling with functions, multiple modelling viewpoints, modelling time, common modelling patterns, model translation, and debugging discrete optimisation models. We will examine complex real world problems and see how they can be translated so that they can be solved by modern discrete optimisation technology.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| First semester | Malaysia | On campus |
| First semester | Suzhou (SEU) | On campus |
Assessment
- In class participationParticipationThreshold hurdle10%
- Assignment 1AssignmentThreshold hurdle5%
- Assignment 2AssignmentThreshold hurdle15%
- Mid-semester testOtherThreshold hurdle5%
- Assignment 3AssignmentThreshold hurdle25%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle40%
Learning outcomes
When you finish this unit, you should be able to:
- 1
model a discrete optimisation problem using a mix of basic and more advanced modelling techniques in a high level modelling language;
- 2
interpret and explain models written by others;
- 3
explain how models are mapped to solver-level input;
- 4
identify and fix errors in models;
- 5
evaluate the limitations, appropriateness and benefits of different modelling patterns for common problem classes;
- 6
evaluate and improve the efficiency of models by applying different model transformations.
Workload and teaching
- Laboratories24 hours
- Workshops24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Learning resources
Technology resources
All code examples, lab tasks and assignments use the MiniZinc constraint modelling language. MiniZinc is available free from https://www.minizinc.org for Windows, Linux and macOS. We recommend that you install MiniZinc on your own laptop, however you can also access it on MoVE at https://move.monash.edu.
Where it fits
FIT5216 is part of 2 areas of study in the 2024 handbook.
Contacts
- Chief Examiners
- Professor Peter Stuckey
- Unit Coordinators
- Dr Arghya Pal
Common questions
What are the prerequisites for FIT5216?
You need FIT9133, FIT9136 or FIT9131 before you enrol. Enrolment rules also apply.
When is FIT5216 offered?
In 2024, FIT5216 runs in Semester 1 at Clayton, Malaysia and Suzhou (SEU).
How much work is FIT5216?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5216 have an exam?
Yes. The exam is worth 40% of the final mark, alongside 5 other tasks.
Which majors and minors include FIT5216?
FIT5216 is part of Computational science and Software engineering.