A review of scenario generation methods
type: Item
instance of: scholarly article
title: A review of scenario generation methods
zbMATH Open document ID: 1206.90110
DOI: 10.1504/IJCSM.2010.037446
author: Sovan Mitra
author: Nico Di Domenica
published in: International Journal of Computing Science and Mathematics
publication date: 2011-02-04T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C15
zbMATH DE Number: 5845436
zbMATH Keywords: stochastic programming
zbMATH Keywords: stochastic optimisation
zbMATH Keywords: scenario generation
zbMATH Keywords: scenarios
zbMATH Keywords: decision making
zbMATH Keywords: uncertainty
MaRDI profile type: Publication
full work available at URL: ijcsm.2010.037446
OpenAlex ID: W2052696840
description: scientific article; zbMATH DE number 5845436