The course covers a range of techniques from management science that have a practical relevance to business decision making. The aim of the course is to develop in students the ability to apply techniques from management science to problems in business where outcomes may be uncertain or where the problem is not well defined.
Decision Making:
- Decision Trees.
- Decision Rules of Thumb.
- Dealing with uncertainty.
- Bayesian analysis.
- Expected value of perfect and imperfect information.
Multicriteria Decision-Making (MCDM):
- The Analytical Hierarchy Process.
- Pairwise Comparison. Synthesisation.
- Ranking.
- Recent Trends in Multicriteria.
- Decision Analysis (MCDA) and MCDM.
Forecasting:
- Probability models for time series.
- Univariate and multivariate Approaches to Forecasting.
- Evaluation of Forecasting.
- Forecasting Error.
- Scenario Planning.
Simulation:
- The Monte Carlo Process.
- Building a Simulation Model.
- Continuous Probability Distributions.
- Statistical Analysis of Simulation Results.
- Model Verification.
- Areas of Application of Simulation.
Decision Making
Expected Value, Payoff Tables, Opportunity Loss Tables, Maximax, Maximin and Minimax rules, Decision Trees
Dealing with uncertainty and perfect infromation
Revising probabilities using Bayesian analysis. Expected value of perfect and imperfect information.
Multicriteria Decision-Making (MCDM
Goal programing The Analytical Hierarchy Process. Pairwise Comparison. Synthesisation. Ranking.Consistency
Simulation
The Monte Carlo Process. Building a Simulation Model. Continuous Probability Distributions. Statistical Analysis of Simulation Results. Model Verification. Areas of Application of Simulation.
The module will be delivered primarily through lectures, tutorials and laboratory work.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 60 |
| Other Assessment(s) | 40 |