Course Title: Practice of Optimisation

Part A: Course Overview

Course Title: Practice of Optimisation

Credit Points: 12.00

Important Information:

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209

Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus 


Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 



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 (Pre-requisites) 

  • MATH2390 Optimisation or equivalent course covering introductory discrete optimisation or integer programming.
  • 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. 


Course Learning Outcomes (CLOs)

On 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

The outcomes for this course are best achieved through hands-on experience. Introductory online lecture materials will cover some basic mathematical techniques required to formulate mixed integer programming problems. An introduction to the principles behind solution methods will be presented for you to understand the impact of your model formulations. An illustration will be presented on how to solve an MIP model using Python and a commercial software package. After that, numerous real-life problems from a diverse set of practical applications will be presented and discussed in detail. You will have regular opportunities to practice your model formulation and problem-solving skills through the practical assessments over the semester.


Overview of Learning Resources

A recommended reading list will be provided. It is possible to undertake all the assessments using commercial software that is freely available to students, and you will need access to a personal computer (Windows / Mac / Linux) to download, install and use this software. Note that Smartphones, Apple/Android tablets or Chromebooks are not suitable for use with the commercial optimisation software.  

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


Overview of Assessment

Note that:  This course has no hurdle requirements.  

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 and 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, and 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, and 5