MATH268 :
OPERATIONAL RESEARCH; PROBABILISTIC MODELS
AIMS:
To introduce a range of models and techniques for solving
under
uncertainty in Business, Industry, and Finance.
LEARNING OUTCOMES:
After completing the module, the students should be familiar
with a range of techniques for solving probabilistic problems arising in OR.
OUTLINE SYLLABUS:
Decision
analysis: strategies and decision trees; elements of Markov
Decision Processes.
Forecasting:
errors and accuracy, moving average, exponential smoothing, seasonal
effects.
Queuing Theory:
M/M/1, M/M/K and similar Markov models.
Simulation:
pseudo-random generators, universal and special algorithms (Polar, Von
Neumann, etc). Simulation of stochastic processes
including queues.
RECOMMENDED TEXT:
A. S. C. Ehrenberg, A Primer in Data Reduction, Wiley, 1982.
F. S. Hillier and G. J. Liebermann, Operations Research, 6th ed, McGraw-Hill, 1995
ASSESSMENT WEIGHTINGS: 90% Examination; 10% Continuous Assignment.
MATH RESOURCES:
Basic
formulas
Past exam papers
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