Risk management is the process that involves identifying, selecting, and implementing measures that can be applied to mitigate the risk in a particular situation. The objective of risk management, in this context, is to find the set of actions to reduce the risk to acceptable levels as defined by the analyst or risk manager. In order to help achieve this objective, many techniques have been developed, both qualitative and quantitative. Among the quantitative techniques, optimization has been at the forefront, having natural appeal as based on a set of objective mathematical formulations for deriving an optimal solution.
In this paper, we describe two prominent optimization techniques that are frequently used in risk management applications for their ability to handle uncertainty in the data, and discuss the advantages and disadvantages of these methods. Next we describe how simulation optimization can overcome the limitations of traditional optimization techniques, and detail some innovative methods that make this a very useful, practical, and intuitive approach for risk management. We conclude by illustrating the advantages of simulation optimization using two practical examples.
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