Course Title: Optimisation for Decision Making

Part A: Course Overview

Course Title: Optimisation for Decision Making

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2468

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2024

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

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

Course Coordinator Location: 15.4.11

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.

Problem-solving

  • 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.

Communication

  • 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.


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

  1. Create, analyse  and solve linear and network optimisation problems using spreadsheets;
  2. Solve complex discrete, and nonlinear optimisation problems using spreadsheets;
  3. Understand the value of having multiple objectives and find solutions that consider them;
  4. Devise Monte Carlo simulation models using spreadsheets and use them to answer questions handling random variables as inputs;
  5. Propose and justify solutions to decision-making problems where there is uncertainty using decision analysis techniques including decision trees.  


Overview of Learning Activities

Pre-recorded lectures will explain concepts and provide guidance on independent learning and embedded tutorials within the lectures will help you master modelling and use of the software package. You will complete regular practical assessment tasks to get instant feedback on your progress and to practice the usage of the software package.


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 
Weighting 25% 
This assessment supports CLO 1 

Assessment Task 2: Practical Assessments – Discrete Optimisation and Decision Analysis,  
Weighting 35% 
This assessment supports CLOs 2 and 5 

Assessment Task 3: Practical Assessments – Nonlinear and Multiobjective Optimisation and Simulation  
Weighting 40% 
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.