Course Title: Practice of Optimisation

Part A: Course Overview

Course Title: Practice of Optimisation

Credit Points: 12.00


Course Coordinator: John Hearne

Course Coordinator Phone: +61 3 9925 2284

Course Coordinator Email: john.hearne@rmit.edu.au

Course Coordinator Location: 8.9.11

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

You are assumed to have completed an introductory course in vector calculus, and be familiar with computing and the writing of simple programs:

Math2311 – Applied Linear Algebra

Math2109 – Mathematical Computing and Algorithms

 

It is recommended to have already completed MATH2390 Optimisation.


Course Description

Optimisation is the core of prescriptive analytics. The aim of this course is to teach you how to formulate and solve optimisation problems that arise in many practical applications. The applications covered in this course will comprise problems from the following domains: planning, loading and cutting, vehicle routing, scheduling and finance. Most of the problems will involve linear integer programming but some nonlinear problems will also be covered. You will mainly use state-of-the-art commercial solvers for solving the problems you formulate but some heuristic methods such as simulated annealing will also be introduced. For these methods, suitable code will be provided and explained


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.  


  1. Utilise the power of optimisation to solve problems for a range of applications
  2. Formulate and solve mixed integer programming (MIP) models using state-of-the-art commercial software
  3. Use some heuristic optimisation methods to solve problems;


Overview of Learning Activities

The outcomes for this course are best achieved through hands-on experience. Introductory lectures will cover some basic mathematical techniques required to formulate mixed integer programming problem. An introduction to the principles behind solution methods will be presented and how this should influence formulation. How to solve models using a commercial software package will be demonstrated. At a later stage an outline of two heuristic methods will be given together with appropriate code.  Other than that, for most of the course numerous real problems will be distributed amongst small groups for their analysis and subsequent report back to the class. This will allow a wide range of problems to be covered while also giving everyone practical problem-solving experience. Some individual assignments will also form part of the course.


Overview of Learning Resources

Use will mainly be made of several free online sources of material. Required software packages will be available in the computer laboratory and for free download on your own computer.

See also the Library Subject Guide

http://rmit.libguides.com/mathstats


Overview of Assessment

Early Assessment Task: Hand in Labs 

Weighting 15%

This assessment task supports CLOs 1 & 2

Assessment Task 2:  Group assignments

Weighting 15%

This assessment task supports CLOs  1 & 2&3

Assessment 3: Lab Exam

Weighting 20% 

This assessment supports CLOs  1, 2 & 3

Assessment 4: Final Exam(written)

Weighting 50% 

This assessment supports CLOs  1, 2 & 3