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Constraint Programming (COMP90046)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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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
  • Global constraints
  • Multiple Modelling
  • Model Debugging
  • Scheduling and Packing
  • Finite domain constraint solving
  • Mixed Integer Programming

Intended learning outcomes

INTENDED LEARNING OUTCOMES (ILO)


On completion of this subject the student is expected to:



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

Generic skills

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.

Last updated: 3 November 2022

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
COMP90048 Declarative Programming
Semester 2 (On Campus - Parkville)
 12.5

And

Code Name Teaching period Credit Points
COMP90038 Algorithms and Complexity
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5

OR Equivalent

Corequisites

None

Non-allowed subjects

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

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

  • There are five project assignments done during the semester, requiring approximately 35 – 40 hours in total, due approximately every two weeks, (30%).
  • 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

Last updated: 3 November 2022

Dates & times

  • Semester 2
    Principal coordinatorPeter Stuckey
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week
    Total time commitment200 hours
    Teaching period24 July 2017 to 22 October 2017
    Last self-enrol date 4 August 2017
    Census date31 August 2017
    Last date to withdraw without fail22 September 2017
    Assessment period ends17 November 2017

    Semester 2 contact information

    email: pstuckey@unimelb.edu.au

Time commitment details

200 hours

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    None

  • Subject notes

    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.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Data Science
    CourseMaster of Science (Computer Science)
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
    CourseMaster of Information Technology
    CourseMaster of Information Technology
    CoursePh.D.- Engineering
    Informal specialisationComputer Science
    Informal specialisationMaster of Engineering (Mechatronics)
    MajorMIT Computing Specialisation
    Informal specialisationMaster of Engineering (Software)
    Specialisation (formal)Software
    Specialisation (formal)Mechatronics
    MajorMIT Distributed Computing Specialisation
    MajorComputer Science
    Specialisation (formal)Computing
    Specialisation (formal)Distributed Computing
  • 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.

    Additional information for this subject

    Subject coordinator approval required

Last updated: 3 November 2022