Research Proposal: Meta-heuristics in Robust Optimisation

我的研究是基于开发在不确定性下执行优化的方法。我研究一般问题。给定某种形式的模型，用于支持明智的决策（例如，某个问题的物流解决方案的模型，或铁路调度问题的模型......）。优化用于识别模型中将产生最佳结果（即，最佳后勤解决方案或最佳时间表）的值。然而，我们假设问题中存在一些不确定性，因此我们希望避免得到一个好的结果，如果我们可以非常准确地定义解决方案，但高度敏感-因此，如果我们改变解决方案的一小部分，结果就会恶化很多。我们对不确定性所做的假设将我们的工作置于“稳健优化”的范畴之下。因为我们不对我们的方法可以应用于的模型的性质做任何假设，所以我们在“元分析”（通用的基于规则的方法）领域工作。这是相反的，例如，马克的工作主要是在“数学规划”-有一些具体的假设是关于模型，你想应用优化，这是一个限制，但意味着你可能更好地能够找到一个最佳的解决方案比你可以为我更一般类型的方法。

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.