Optimisation

Subject 436-414 (2008)

Note: This is an archived Handbook entry from 2008.Search for this in the current handbook

Credit Points: 12.500
Level: Undergraduate
Dates & Locations:

This subject has the following teaching availabilities in 2008:

Semester 2, - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: Twenty-four lectures and 24 hours of tutorial/projects/practice classes
Total Time Commitment: Not available
Prerequisites: 431-201 Engineering Analysis A and 431-202 Engineering Analysis B; or 620-231 Vector Analysis and 620-232 Math Methods and 620-331 Applied PDE's.
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

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

Coordinator

Assoc Prof S Halgamuge
Subject Overview:

Upon completion, students should be able to model and solve a range of decision-making problems in Mechanical, Biomedical and Mechatronic engineering by applying the techniques of mathematical programming, stochastic modelling and Optimisation.

Topics covered include modeling and optimization methods in Artificial Intelligence, decision theory, basics of Convex Optimisation, queuing models and Markov processes.

Assessment: One 3-hour end-of-semester examination (70%); one written project report of up to 6000 words with no more than 10 pages of supporting material (appendices, diagrams, tables etc) due towards the end of the semester (30%).
Prescribed Texts: None
Recommended Texts:

Information Not Available
Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

  • ability to apply knowledge of basic science and engineering fundamentals

  • in-depth technical competence in at least one engineering discipline

  • ability to undertake problem identification, formulation and solution

  • ability to utilise a systems approach to design and operational performance

  • capacity for independent critical thought, rational inquiry and self-directed learning
Related Course(s): Bachelor of Engineering (EngineeringManagement)Mechanical&Manufacturing
Bachelor of Engineering (Mechanical &Manufacturing)& Bachelor of Science
Bachelor of Engineering (Mechanical &Manufacturing)/Bachelor of Commerce
Bachelor of Engineering (Mechanical and Manufacturing Engineering)
Bachelor of Engineering (Mechatronics) and Bachelor of Computer Science

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