Zverovich, V.
[Author];
Fábièn, C.
[Author];
Ellison, F.
[Author];
Mitra, G.
[Author]
;
Higle, Julie L.
[Contributor];
Römisch, Werner
[Contributor];
Sen, Surrajeet
[Contributor]
A computational study of a solver system for processing two-stage stochastic linear programming problems
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Media type:
E-Book
Title:
A computational study of a solver system for processing two-stage stochastic linear programming problems
Contributor:
Zverovich, V.
[Author];
Fábièn, C.
[Author];
Ellison, F.
[Author];
Mitra, G.
[Author]
imprint:
Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik, 2009-05-19
Language:
English
DOI:
https://doi.org/10.18452/8401
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Formulation of stochastic optimisation problems and computational algorithms for their solution continue to make steady progress as can be seenfrom an analysis of many developments in this field. The edited volume by Wallace and Ziemba (2005) outlines both the SP modelling systems andmany applications in diverse domains.More recently, Fabozzi (2008) has considered the application of SP models to challenging financial engineering problems. The tightly knit yet highlyfocused group of researchers COSP: Committee on Stochastic Programming, their triennial international SP conference, and their active website points to the progressive acceptance of SP as a valuable decision tool. At the same time many of the major software vendors, namely, XPRESS, AIMMS, and MAXIMAL GAMS have started offering SP extensions to their optimisation suites.Our analysis of the modelling and algorithmic solver requirements reveals that (a) modelling support (b) scenario generation and (c) solutionmethods are three important aspects of a working SP system. Our research is focussed on all three aspects and we refer the readers to Valente et al.(2009) for modelling and Mitra et al. (2007) and Di Domenica et al. (2009) for scenario generation. In this paper we are concerned entirely with com-putational solution methods. Given the tremendous advance in LP solver algorithms there is certain amount of complacency that by constructing a”deterministic equivalent” problems it is possible to process most realistic instances of SP problems. In this paper we highlight the shortcoming of this line of argument. We describe the implementation and refinement of established algorithmic methods and report a computational study which clearly underpins the superior scale up properties of the solution methods which aredescribed in this paper.A taxonomy of the important class of SP problems may be found in Valente et al. (2008, 2009). The most important class of problems with manyapplications is the two-stage stochastic programming model with recourse, which ...