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)

MATH1293

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 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

MATH1293

City Campus

Postgraduate

171H School of Science

Face-to-Face

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

MATH2055

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2022

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

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

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

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.


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

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

A list of recommended text will be provided.

You will have access to extensive course materials made available via the online RMIT Learning Hub (myRMIT), including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources. You can access course software through myDesktop.

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


Overview of Assessment

This course has no hurdle requirements.

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