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GasLib—A Library of Gas Network Instances

1
Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
2
Energie Campus Nürnberg, Fürther Str. 250, 90429 Nürnberg, Germany
3
Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany
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Institut für Mathematische Optimierung, Technische Universität Braunschweig, Universitätsplatz 2, 38106 Braunschweig, Germany
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School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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Institut für Angewandte Mathematik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany
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Department of Mathematics, Technische Universität Darmstadt, Dolivostr. 15, 64293 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 18 November 2017 / Accepted: 23 November 2017 / Published: 1 December 2017

Abstract

The development of mathematical simulation and optimization models and algorithms for solving gas transport problems is an active field of research. In order to test and compare these models and algorithms, gas network instances together with demand data are needed. The goal of GasLib is to provide a set of publicly available gas network instances that can be used by researchers in the field of gas transport. The advantages are that researchers save time by using these instances and that different models and algorithms can be compared on the same specified test sets. The library instances are encoded in an XML (extensible markup language) format. In this paper, we explain this format and present the instances that are available in the library. View Full-Text
Keywords: gas transport; networks; problem instances; mixed-integer nonlinear optimization; GasLib gas transport; networks; problem instances; mixed-integer nonlinear optimization; GasLib
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Schmidt, M.; Aßmann, D.; Burlacu, R.; Humpola, J.; Joormann, I.; Kanelakis, N.; Koch, T.; Oucherif, D.; Pfetsch, M.E.; Schewe, L.; Schwarz, R.; Sirvent, M. GasLib—A Library of Gas Network Instances. Data 2017, 2, 40.

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