Models for decision making: an overview of problems tools and major issues
Mathematical models for decision support
Barycentric scenario trees in convex multistage stochastic programming
Mathematical Programming: Series A and B
Data Warehousing, Data Mining, and Olap
Data Warehousing, Data Mining, and Olap
A Heuristic for Moment-Matching Scenario Generation
Computational Optimization and Applications
Journal of Global Optimization
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
STORM: A Statistical Object Representation Model
Proceedings of the 5th International Conference SSDBM on Statistical and Scientific Database Management
An Adaptive Dynamic Programming Algorithm for the Heterogeneous Resource Allocation Problem
Transportation Science
Multidimensional Data Modeling for Complex Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Generating Scenario Trees for Multistage Decision Problems
Management Science
Introduction to Stochastic Programming
Introduction to Stochastic Programming
Knowledge-based scenario management - Process and support
Decision Support Systems
A scenario generation framework for automating instructional support in scenario-based training
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
A decision support system for strategic asset allocation
Decision Support Systems
Designing decision support systems for value-based management: A survey and an architecture
Decision Support Systems
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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 this paper, we investigate the role of such a tool within the context of a combined information and decision support system. We explain how two well-developed modelling paradigms, decision models and simulation models can be combined to create ''business analytics'' which is based on ex-ante decision and ex-post evaluation. We also examine how these models 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. In this paper, our main objective is to explain to the information systems (IS) community how advanced models and their software realisations can be integrated with advanced IS and DSS tools.