Course Title: Probabilistic Models in Operations Research

Part A: Course Overview

Course Title: Probabilistic Models in Operations Research

Credit Points: 12.00

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
MATH1290	City Campus	Undergraduate	145H Mathematical & Geospatial Sciences	Face-to-Face	Sem 1 2010

Course Coordinator: Dr. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

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

Pre-requisite Courses and Assumed Knowledge and Capabilities

Some basic knowledge of mathematical modelling and probability theory.

Course Description

Probabilistic models are among the most widely used models in operations research and management science. Probabilistic models and related tools, 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 decision making under uncertainty, inventory models, Markov chains, queuing theory, and simulation.

Objectives/Learning Outcomes/Capability Development

  The objectives of the course are:
• to develop skills in building probabilistic models using Markov chains and simulation;
• to better understand inventory/production control in light of probabilistic models.
• to develop an understanding of queuing systems under different configurations.
•to become familiar with the use of commercial software packages that allows us to simulate complex systems.
• to develop skills in analysing and interpreting the solutions.

 At the conclusion of this course, you will :
• have an appreciation for the power of probabilistic models and its range of applications;
• master essential probabilistic modelling tools including Markov chains, queuing theory, and simulation
• be able to formulate and solve problems which involve setting up probabilistic models;
• be able to implement simulation of complex models using commercial software;
• be able to interpret the outputs of simulation exercises.

Overview of Learning Activities

 The objectives of this course are best learnt through lectures and computer laboratory hours with hands-on experience. After a brief review of probability theory and mathematical modelling, decision making under uncertainty, inventory models, Markov chains, queuing theory and simulation will be covered in detail during lectures. Various simulation and probabilistic optimisation software will be demonstrated during laboratory hours. Group assignments will be distributed, contributing to the learning process by giving students the opportunity to model and solve various problems.

Overview of Learning Resources

A recommended reading list will be provided. A number of commercial software packages will be available in the computer laboratory. Student versions of the software for home use may be available depending on the software licence requirements.

Overview of Assessment

Assessment is through assignments, tests, and examinations. While attendance at lectures is not compulsory, you will find that regular attendance is necessary as lectures and computer laboratory hours will be important aspects of the learning experience.