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There are numerous mathematical methods used to obtain efficient solutions to a large variety of complex business problems. This subject provides students with the ability to formulate a business problem as a mathematical model, and then to use quantitative techniques to identify a solution to the model. Topics covered include linear programming, duality, integer programming, mixed integer programming, non-linear programming, Monte Carlo simulations, decision trees, expected value of perfect information, sample information and control, and sensitivity analysis.
Intended learning outcomes
On completion of this subject, students should be able to:
- Recognise when business problems can be quantitatively modelled, and then formulate suitable models for those problems.
- Understand the most relevant methods, and the limitations and trade-offs between different methods required to solve these models.
- Perform optimisation techniques to analyse how constraints affect the achievement of business objectives.
- Use sensitivity analysis to identify the level of tolerance permissible in managerial decision making.
- Evaluate the role of risk and uncertainty in managerial decision making, with regard to the quantity and quality of information available for analysis
Last updated: 10 November 2019