High-Performance Prototyping of Decomposition Methods in GAMS

Timo Lohmann, Michael R. Bussieck (), Lutz Westermann () and Steffen Rebennack ()
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Timo Lohmann: Uniper Global Commodities SE, 40221 Düsseldorf, Germany
Michael R. Bussieck: GAMS Development Corp., Fairfax, Virginia 22031
Lutz Westermann: GAMS Software GmbH, 38102 Braunschweig, Germany
Steffen Rebennack: Karlsruhe Institute of Technology, Institute of Operations Research, 76131 Karlsruhe, Germany

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 34-50

Abstract: Prototyping algorithms in algebraic modeling languages has a long tradition. Despite the convenient prototyping platform that modeling languages offer, they are typically seen as rather inefficient with regard to repeatedly solving mathematical programming problems, a concept on which many algorithms are based. The most prominent examples of such algorithms are decomposition methods, such as the Benders decomposition, column generation, and the Dantzig–Wolfe decomposition. In this work, we discuss the underlying reasons for repeated solve deficiency with regard to speed in detail and provide an insider’s look into the algebraic modeling language GAMS. Further, we present recently added features in GAMS that mitigate some of the efficiency drawbacks inherent to the way modeling languages represent model data and ultimately solve a model. In particular, we demonstrate the grid-enabled gather-update-solve-scatter facility and the GAMS object-oriented application programming interface on a large-scale case study that involves a Benders decomposition–type algorithm for a power-expansion planning problem.

Keywords: algebraic modeling languages; GAMS; data structures; parallel computing; Benders decomposition; expansion planning (search for similar items in EconPapers)
Date: 2021
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https://doi.org/10.1287/ijoc.2019.0905 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:34-50

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