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Stochastic programming and scenario generation within a simulation framework: An information systems perspective
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DOI：10.1016/j.dss.2006.06.013.png)
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Abstract
En 中文
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. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
scenario generation
stochastic programming
DSS
OLAP
business analytics
simulation
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