Course Title: Optimisation for Decision Making

Part A: Course Overview

Course Title: Optimisation for Decision Making

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016


City Campus


171H School of Science


Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022,
Sem 2 2023


City Campus


171H School of Science


Sem 2 2022

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

Course Coordinator Email:

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge

Basic Microsoft Excel knowledge

Course Description

This course introduces approaches to solving optimisation problems faced by decision makers in today’s fast-paced business environment through building computer models to analyse and evaluate decision alternatives. By applying the methods and tools of science to management and decision making, sensible courses of action may be devised for real world problems. Extensive use will be made of appropriate software for problem solving, principally with spreadsheets.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


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


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

On completion of this course you should be able to:

  1. Create and solve linear and network optimisation problems using spreadsheets and investigate the sensitivity of the solution(s) to the assumptions of the model;
  2. Solve discrete, nonlinear and multi-objective optimisation problems using spreadsheets;
  3. Devise simulation models using spreadsheets and use them to answer questions;
  4. Propose and justify solutions to decision making problems where there is uncertainty using decision trees and other decision analysis approaches
  5. Apply project management techniques to identify critical stages and tasks of a project.

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.

Overview of Assessment

Assessment Tasks:

Assessment Task 1: Practical Assessments – Linear and Network Optimisation, Sensitivity Analysis
Weighting 33%
This assessment supports CLO 1

Assessment Task 2: Practical Assessments – Discrete, Nonlinear and Multiobjective Optimisation, Project Management
Weighting 33%
This assessment supports CLOs 2 and 5

Assessment Task 3: Practical Assessments – Simulation and Decision Analysis
Weighting 34%
This assessment supports CLOs 3 and 4

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.