Units / FIT5216
FIT5216 · Modelling discrete optimisation problems
2027 Handbook6 credit pointsLevel 5Faculty of Information Technology
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
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Assignment 1 | Exercise | 5% | — |
| 2 | Assignment 2 | Project | 10% | — |
| 3 | Assignment 3 | Project | 20% | — |
| 4 | Mid-semester test | Quiz / Test | 0% | Competency |
| 5 | Workshop participation | Exercise | 5% | — |
| 6 | Scheduled final assessment (2 hours and 10 minutes) | Examination | 60% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prohibition
Learning outcomes
- model a discrete optimisation problem using a mix of basic and more advanced modelling techniques in a high level modelling language;
- interpret and explain models written by others;
- explain how models are mapped to solver-level input;
- identify and fix errors in models;
- evaluate the limitations, appropriateness and benefits of different modelling patterns for common problem classes;
- evaluate and improve the efficiency of models by applying different model transformations.
Workload
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.
| Activity | Duration |
|---|---|
| Workshops | 24 hours |
| Laboratories | 24 hours |
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