Modelling and Solving Techniques for Stochastic Combinatorial Optimisation Problems

Decision making under uncertainty is an important topic in many Industries, such as telecommunication, logistics and energy management. For example, scheduling electricity generators in light of demand and production uncertainties is almost impossible without the help of computers. We tackle such decision problems using a two-step approach. First, we write a model of the problem using mathematics, and secondly, we deploy appropriate algorithms to find a solution to the problem. The contribution of this thesis is two-fold, first we propose techniques to improve mathematical models, and secondly, we present algorithmic innovations to find high quality solutions quickly.