6122 (v.5) Combinatorial Optimisation 502

Area:

Department of Mathematics and Statistics

Credits:

25.0

Contact Hours:

3.0

Lecture:

3 x 1 Hours Weekly

Prerequisite(s):

6121 (v.5) Advanced Optimisation Techniques 501 or any previous version

Syllabus:

Matching and Processor Scheduling. Hungarian Method, Edmond's Algorithm. Network Flow Theory. Minimum cost flow problem and an Algorithm, Project cost curve - an application in project management. Combinatorial Optimisation. Lagrangian Relaxation, Bender's decomposition, Subgradient optimization. Integral Polyhedra. Totally unimodular matrices, Network matrices, Balanced matrices and their applications.

Unit Outcomes:

On successful completion of this unit the student will be able to recognise problems from Industry and government that can be mathematically modelled as a combinatorial optimization problem. Independently develop analytical solution methods to solve such industry problems. Independently learn new frontiers of combinatorial optimization to pursue applications and research. Writing skills to present applications of mathematics and mathematical ideas in a logical manner.

Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.

Unit References:

Ford, L.R. and Fulkerson, D.R., 1962. "Flows in Networks", Princeton Press. Garfinkel, R.S. and Nemhauser, G.L,, 1972. "Inter Programming", Wiley. Hillier, F.S and Lieberman, G.T., 1980. "Introduction to Operations Research", 3rd Edition, Holden Day. Papadimitriou, C.H. and Steiglitz, K., 1982. "Combinatorial Optimisation, Algorithms and Complexity", Prentice-Hall Inc. Winston, W.L., "Operations Research : Applications and Algorithms", 1994. Duxbury Press, Wadsworth.

Unit Texts:

None required.

Unit Assessment Breakdown:

Assignments 40% and Examination 60%

Year	Location	Period	Internal	Area External	Central External
2004	Bentley Campus	Semester 1	Y
2004	Bentley Campus	Semester 2	Y