Course Title: Stochastic Processes and Applications

Part A: Course Overview

Course Title: Stochastic Processes and Applications

Credit Points: 12.00


Course Coordinator: Dr Xu Zhang

Course Coordinator Phone: +61 3 9925 2000

Course Coordinator Email: xu.zhang@rmit.edu.au

Course Coordinator Availability: by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Basic knowledge of mathematical modelling and probability theory.


Course Description

Stochastic models are among the most widely used tools in operations research and management science. Stochastic processes and applications can be used to analyse and solve a diverse range of problems arising in production & inventory control, resource planning, service systems, computer networks and many others. This course, with an emphasis on model building, covers inventory models, Markov chains, Poisson processes and queuing theory.


Objectives/Learning Outcomes/Capability Development

Course Learning Outcomes:

On completion of this course you should be able to:

  • Elucidate the power of stochastic processes and their range of applications;
  • Demonstrate essential stochastic modelling tools including Markov chains and queuing theory;
  • Formulate and solve problems which involve setting up stochastic models


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Overview of Learning Activities

The objectives of this course are best learnt through lectures and class exercises. After a brief review of probability theory and mathematical modelling, the topics of decision making under uncertainty, inventory models, Poisson processes, Markov chains and queuing theory will be covered in detail during lectures. While attendance at lectures is not compulsory, you will find that regular attendance is necessary as lectures will be important aspects of the learning experience.

The course is supported by the Blackboard learning system. Assessment comprises class exercises, a test and an exam. 


Overview of Learning Resources

Some basic lecture notes for this course will be available on Blackboard. A recommended reading list will also be provided.

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:  Class Exercises
Weighting 25%
This assessment task supports CLOs 1,2 & 3

Assessment Task 2: Mid Semester Test
Weighting 25%
This assessment task supports CLO 1,2 &3

Assessment Task 3: Final Exam
Weighting 50% 
This assessment supports CLO 1,2 & 3