Course Title: Linear Programming and Modelling

Part A: Course Overview

Course Title: Linear Programming and Modelling

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1288

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

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

MATH1288

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2019

Course Coordinator: Prof. Andrew Eberhard

Course Coordinator Phone: +(61 3) 9925 2616

Course Coordinator Email: andrew.eberhard@rmit.edu.au

Course Coordinator Location: 8.9.26


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed the following courses before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning. 


Course Description

This course introduces you to the fundamentals of Operations Research Models including linear programming and applications. You will learn how to construct models appropriate to particular applications, develop optimal solutions, understand the theory behind solutions and translate solutions into directives for action. On successful completion of this course you will be able to: define and formulate linear programming problems and appreciate their limitations; solve linear programming problems using appropriate techniques and interpret the results obtained; conduct and interpret post-optimal and sensitivity analysis; and  explain the primal-dual relationship.


Objectives/Learning Outcomes/Capability Development

On completion of this course you should be able to:


1. Define and formulate linear programming problems and appreciate their limitations.
2. Solve linear programming problems using appropriate techniques and optimization solvers, interpret the results obtained and translate solutions into directives for action.
3. Conduct and interpret post-optimal and sensitivity analysis and explain the primal-dual relationship.
4. Develop mathematical skills to analyse and solve integer programming and network models arising from a wide range of applications. 
5. Effectively communicate ideas, explain procedures and interpret results and solutions in written and electronic forms to different audiences. 
 


This course contributes to the development of the following Program Learning Outcomes:

Knowledge and technical competence

  • Coherent and advanced knowledge of the underlying principles and concepts in one or more disciplines.

Problem-solving

  • Cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.

Communication

  • Communication skills to present clear and coherent exposition of knowledge and ideas to a variety of audiences.

Ethics

  • Application of knowledge and skills with responsibility and accountability for own learning and professional practice, and in collaborations with others within broad parameters.


Overview of Learning Activities

Lectures will be devoted to teaching the theoretical concepts and computational methods of linear programming and applications. Tutorial and computer laboratory classes will provide the practical skills needed to operate solvers for linear programming problems. You will develop abilities to think critically and analytically to address more challenging optimisation problems. Individual assignments will give you practice in formulating and solving mathematical programming formulations and interpreting the results using software. Regular exercises will enhance your learning through timely hands-on practice and feedback. A mid semester exam will give you the opportunity to track your progress and identify the areas for improvement.

 

Teacher Guided Hours: 60 per semester
Learner Directed Hours: 60 per semester


Overview of Learning Resources

Learning resources consist of  a set of online learning resources including a typeset textbook and a set of lecture slides, references, weekly exercise questions and solutions will be made available on CANVAS.


Overview of Assessment

 This course has no hurdle requirements.


Assessment Tasks:

Early Assessment Task: Class Exercises 1 and 2
Weighting 4%
This assessment task supports CLOs 1 & 2
Assessment Task 2:  Assignments
Weighting 12%
This assessment task supports CLOs 1, 2, 3, 4 and 5

Assessment Task 3:  Class Exercises 3 to 9
Weighting 14%
This assessment task supports CLOs 1, 2, 3, & 4

Assessment Task 4: Mid semester test
Weighting 20%
This assessment task supports CLOs 1 & 2

Assessment 5: Final exam
Weighting 50%  
This assessment task supports CLOs 1, 2, 3 & 4