In stochastic optimization problems, uncertainty is normally represented by means of a scenario tree. Finding an accurate representation of this uncertainty when dealing with a set of historical series is an important issue, because of its influence in the results of the above mentioned problems. This article uses a procedure to create the scenario tree divided into two phases: the first one produces a tree that represents accurately the original probability distribution, and in the second phase that tree is reduced to make it tractable. Several clustering methods are analysed and proposed in the paper to obtain the scenario tree. Specifically, these are applied to an academic case and to natural hydro inflows series, and comparisons amongst them are established according to these results.
Palabras clave: Scenario tree generation; Uncertainty modelling; Stochastic programming
European Journal of Operational Research. Volumen: 181 Numero: 3 Páginas: 1339-1353
Índice de impacto JCR y cuartil Scopus: JCR impact factor: 1.096 (2007); 3.806 (2018).
Referencia DOI: 10.1016/j.ejor.2005.11.045
Publicado en papel: Septiembre 2007.
J.M. Latorre, S. Cerisola, A. Ramos. Clustering algorithms for scenario tree generation: Application to natural hydro inflows. European Journal of Operational Research. vol. 181, no. 3, pp. 1339-1353, Septiembre 2007.