This unit is a core elective in the Master of Artificial Intelligence to be introduced in 2020.

Discrete Optimisation technology is key for providing good solutions to decision making problems that appear in every area of our lives. It is a research strength of the Faculty and has led to successful collaboration with many industries, including Melbourne Water and Woodside. Yet, FIT does not have any units that teach modern optimisation modelling and solving methods at either the UG or PG level. This misalignment contributes to the lack of access our research group has to HDR students with the appropriate background.

29/10/2019: Updating the prerequisites to include new foundation unit. Effective S1, 2020.

11/11/2019: Updating the prerequisites. Now that planning is more advanced, it was agreed that FIT5216 should become the prereq. Effective 2020.

18/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

On successful completion of this unit, students should be able to:

1. design efficient solutions for discrete optimisation problems;
2. evaluate the limitations, appropriateness and benefits of different solving technologies for particular discrete optimisation problems;
3. define and explore different complete and local search strategies for solving a given problem;
4. explain how modelling interacts with solving technologies, and formulate models to take advantage of this using state of the art optimisation tools.

020307 Decision Support Systems

This unit introduces the fundamental algorithms for solving discrete ptimization problems, such as constraint programming, boolean satisfiability, mixed integer linear programming and local search.

Technological requirements

All code examples, lab tasks and assignments use the MiniZinc constraint modelling language and the Python programming language. MiniZinc is available free from https://www.minizinc.org for Windows, Linux and macOS. We recommend that you install MiniZinc and Python on your own laptop, however you can also these access these through MoVE (Monash Virtual Environment).

On-campus

Active learning

Online learning

Peer assisted learning

Problem-based learning

Examination (2 hours): 40%; In-semester assessment: 60%

1. In-class participation - 10% | Weeks 2-8 during workshops
2. Assignment 1 - 5% - ULO: 1, 2
3. Assignment 2 - 15% - ULO: 1, 2, 3
4. Mid-semester test - 10% - ULO: 1, 2, 3, 4
5. Assignment 3 - 20% - ULO: 1, 2, 3, 4
6. Examination - 40% - ULO: 1, 2, 3, 4

Minimum total expected workload equals 12 hours per week comprising:

• Two hours/week lectures
• Two hours/week laboratories

A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision.

FIT

FIT5216

Graeme Gange