A review of scenario generation methods

Authors:
Sovan Mitra;Nico Di Domenica
Affiliations:
Glasgow Caledonian University, Caledonian Business School, Cowcaddens Road, Glasgow, G4 0BA, Scotland, UK.;Value Lab, Via Durini, Milano, Italy
Venue:
International Journal of Computing Science and Mathematics
Year:
2010

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Abstract

Stochastic programming models provide a powerful paradigm for decision making under uncertainty. In these models the uncertainties are captured by scenario generation and so are crucial to the quality of solutions obtained. Presently there do not exist many literature reviews on scenario generation; this paper surveys them. We introduce the main concepts behind scenario generation, which are not just concerned with discretising methods. We review the main scenario generation classes and analyse the advantages and disadvantages. We also review new and less commonly known scenario generation methods, such as 'hybrid' methods.