Course Title: Practice of Optimisation

Part A: Course Overview

Course Title: Practice of Optimisation

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2396

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 1 2022

Course Coordinator: Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

Course Coordinator Email: melih.ozlen@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed following course/s before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.

Assumed Knowledge

Elementary knowledge of Python (or commitment to learn at the start of the semester)


Course Description

Optimisation models are amongst the most widely used models in analytics and data science. They are used to solve a diverse range of problems comprising telecommunications, transport, timetabling, scheduling, workforce planning, loading, cutting and more. This course concentrates on formulating and building such real-life models, solving them using Python and commercial optimisation software and interpreting their solution.  The course will also introduce more advanced methods useful for solving large scale optimisation problems. 


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Applied Mathematics and Statistics and BH119 Bachelor of Analytics (Honours):

Knowledge and Technical Competence:

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving:

  • synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution. 


Upon successful completion of this course, you should be able to: 

  1. Utilise the power of mathematical programming and its range of applications; 
  2. Formulate and solve advanced real life mixed integer programming (MIP) problems using Python; 
  3. Implement MIP models using Python and commercial software; 
  4. Use heuristic optimisation methods to solve challenging problems; 
  5. Interpret the solution to MIP problems. 


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats 


Overview of Assessment

Assessment Tasks

Assessment Task 1:  Practical Assessment 1 – Formulate and Solve Optimisation Problems using a Solver
Weighting 25%
This assessment task supports CLOs 1, 2, 3 & 5

Assessment Task 2: Practical Assessment 2 – Formulate and Solve Optimisation Problems using a Solver and Column Generation Approach
Weighting 35%
This assessment task supports CLOs 1, 2, 3, 4 & 5

Assessment Task 3: Practical Assessment 3 – Formulate and Solve Optimisation Problems using a Solver and Alternative Approaches
Weighting 40%
This assessment task supports CLOs 1, 2, 3, 4 & 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.