Modelling and analysis of multistage stochastic programming problems: A software environment

In many decision problems, time and uncertainty play important roles and stochastic programming models are well suited for capturing both these aspects. In this paper we focus our attention on the difficulties which arise in developing multi-period stochastic models and we outline those challenging aspects where the use of classical modelling techniques for deterministic problems prove to be inadequate. In particular, we discuss the development of a modelling and analysis environment which combines multidimensional databases (MDDB), declarative modelling languages and procedural languages. Our aim is to develop a versatile tool which generates multi-period stochastic models and supports the modeller in browsing of data and solutions across different time stages and among different scenarios.