Stochastic programming and scenario generation within a simulation framework: An information systems perspective

Stochastic Programming brings together models of optimum resource allocation and models of randomness to create a robust decision making framework. The models of randomness with their finite, discrete realisations are called Scenario Generators. In a compendium report we have considered the modelling perspective of Scenario Generation and its integration within Stochastic Programming. In this paper we investigate the role of such a tool within the context of a combined information and decision support system. We analyse the roles of decision models and descriptive models, and also examine how these can be integrated with data marts of analytic organisational data and decision data. Recent developments in On-Line Analytical Processing (OLAP) tools and multidimensional data viewing are taken into consideration. We finally introduce illustrative examples of optimisation, simulation models and results analysis to explain our multifaceted view of modelling.