Techniques in Operations Research

This subject has the following teaching availabilities in 2010:

One of

• 620-290 Discrete Maths and Operations Research
• 620-261 Introduction to Operations Research (prior to 2009)

This subject introduces some major techniques and algorithms for solving nonlinear optimisation problems. Unconstrained and constrained systems will be considered, for both convex and non-convex problems. The methods covered include: interval search techniques, Newton and quasi-Newton methods, penalty methods for nonlinear programs, and methods based on duality. The emphasis is both on being able to apply and implement the techniques discussed, and on understanding the underlying mathematical principles. Examples involve the formulation of operations research models for linear regression, multi-facility location analysis and network flow optimisation.

A significant part of the subject is the project, where students work in groups on a practical operations research problem.

On completion of this subject students should develop

• skills in setting up operations research models;
• a knowledge of the most important techniques for solving nonlinear optimisation problems;
• an understanding of the role of algorithmic thinking in the solution of operations research problems;
• competence in the use of computer packages in operations research;
• an understanding of the factors and restrictions involved in building and using models for planning and management problems.

Three written assignments due at regular intervals during semester amounting to a total of up to 50 pages (30%), a group project involving a written report of up to 20 pages due at the end of semester (15%) and a 15-minute oral presentation at the end of semester (5%), and a 2-hour written examination in the examination period (50%).

H. A. Taha, Operations Research: An Introduction, McMillan, 5th Ed,1992.

W. L. Winston, Operations Research: Applications and Algorithms, PWS-Kent, 1987.

R. Fletcher, Practical Methods of Optimization, 2nd Ed, John Wiley & Sons, NY, 1987.

This subject potentially can be taken as a breadth subject component for the following courses:

• Bachelor of Arts
• Bachelor of Commerce
• Bachelor of Environments
• Bachelor of Music

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

In addition to learning specific skills that will assist in their future careers in science, students will have the opportunity to develop generic skills that will assist them in any future career path. These include:

• problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
• analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
• collaborative skills: the ability to work in a team;
• time-management skills: the ability to meet regular deadlines while balancing competing commitments;
• computer skills: the ability to use mathematical computing packages.