Constraint Programming

This subject has the following teaching availabilities in 2015:

200 hours

One of the following:

None

None

433-433 Constraint Programming
433-633 Constraint Programming
433-671 Constraint Programming

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability

AIMS

The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc.

INDICATIVE CONTENT

Topics covered will include:

• Modelling with constraints
• Algorithms for manipulating constraints
• Finite domain constraint solving
• Global constraints
• Linear Programming
• Network Flow
• Mixed Integer Programming
• Boolean Satisfiability
• Hybrid constraint solving.

INTENDED LEARNING OUTCOMES (ILO)

On completion of this subject the student is expected to:

1. Model a complex constraint problem using a high level modelling language
2. Define and explore different search strategies for solving a problem
3. Explain how modelling interacts with solving algorithms, and formulate models to take advantage of this using state of the art optimisation tools
4. Explain different optimization technologies, and their strengths and weaknesses

• Project assignments will be done during the semester, requiring approximately 35 – 40 hours in total (30%). There are three projects. The first one is due approximately in the third week and is worth 8% marks, requiring approximately 10 - 12 hours of work. The second project is due around mid-semester and is worth 10% marks, requiring approximately 13 - 15 hours of work. The third project is due at the end of the semester and is worth 12% marks, requiring approximately 15 - 18 hours of work
• One 3-hour end-of-semester examination (70%).

Hurdle requirement: To pass the subject, students must obtain at least:

• 50% overall
• 15/30 in the project assignments
• 35/70 in the end-of-semester written examination.

Intended Learning Outcomes (ILOs) 1, 2, 3, and 4 are addressed in the lectures, laboratory exercises, project assignments and the end-of-semester examination

None

This subject is not available as a breadth subject.

On completion of this subject students should be able to have the following skills:

• Undertake problem identification, formulation, and solution
• Utilise a systems approach to complex problems and to design and for operational performance
• Manage information and documentation in solution creation
• Demonstrate improved capacity for creativity and innovation.

LEARNING AND TEACHING METHODS

The subject comprises a weekly 2 hour lecture followed by a 1 hour laboratory exercise. Weekly readings are assigned from the textbook, and laboratory exercises are assigned. Additionally, a significant amount of project work is assigned.

INDICATIVE KEY LEARNING RESOURCES

At the beginning of the year, the coordinator will propose a textbook on constraint programming and will be made available through University Book Shop and library. The current suggested textbook is

Programming with Constraints: an Introduction. Kim Marriott and Peter J. Stuckey, MIT Press. 1998.

CAREERS / INDUSTRY LINKS

The IT industry is a large and steadily growing industry. Increasingly companies are seeking to use optimization technology to provide decision support, assist in strategic and tactical planning, and manage daily operations. Modelling skills and understanding of optimization technology are essential for working in the optimization industry, for example in optimization consulting companies, or within the strategic planning groups within any major company. Most large companies have many problems that require optimization technology to be solved. Modelling and solving skills are also vital for employees whose role is to tackle these problems.