Modelling and Solving Online Optimisation Problems

Many optimisation problems are of an online nature, where new information arrives and the problem must be resolved periodically in order to (a) improve previous decisions and (b) take the required new ones. Typically, building an online optimisation system requires substantial ad hoc coding, where the optimisation problem is continually adjusted and resolved, keeping track of which previous decisions may be committed and which new decisions need to be taken. In this paper we define a framework for automatically solving online decision problems. This is achieved by extending a model of the offline optimisation problem so that the online version is automatically constructed from this model, requiring no further implementation. In doing so, we formalise many of the aspects that arise in online optimisation problems. The same framework can be applied for automatically creating sliding window solving approaches for problems that extend over a large time domain. Experiments show that we can automatically create efficient online and sliding window solutions to problems.