In this talk, we introduce the framework of optimisation under uncertainty, which consists of collection of disciplines such as stochastic programming, robust optimisation, scenario generation, decomposition methods, and others related. When properly combined, these allow the development of mathematical programming-based decision support tools that can meaningfully consider the inherent uncertainty associated with input data. We illustrate the capabilities of such framework by means of examples derived from real-world problems in which the combination of two or more of these disciplines allowed the development of enhanced models which, ultimately, led to more efficient decision support tools. We will also discuss some of the technical details behind the development of these applications and present future perspectives in terms of research development.