Research Proposal: Meta-heuristics in Robust Optimisation

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My research is based on developing approaches for performing optimisation under uncertainty. I work on general problems. Given some form of model that is being used to support informed decision making (e.g. a model of a logistical solution to some problem, or a model of a railway timetabling problem, ....) optimisation is used to identify the values in the model that will produce the best results i.e. the best logistical solution or best timetable. However we assume that there is some uncertainty in the problem so we want to avoid getting a result that is good if we can define the solution very accurately, but is highly sensitive - so that if we change a small part of the solution the result deteriorates a lot. The assumptions we make about the uncertainty puts our work under a category of 'robust optimisation'. Because we don't make any assumptions about the nature of the models our approaches can be applied to, we are working in the area of 'metaheuristics' (general rule-based approaches). This is as opposed to, for example, Marc's work which is primarily in 'mathematical programming' - there some specific assumptions are made about the models you want to apply optimisation to, which is a limitation, but means that you may be better able to find an optimal solution than you can for my more general types of approach.