GasLib—A Library of Gas Network Instances

Martin Schmidt, Denis Aßmann, Robert Burlacu, Jesco Humpola, Imke Joormann, Nikolaos Kanelakis, Thorsten Koch, Djamal Oucherif, Marc E. Pfetsch, Lars Schewe, Robert Schwarz and Mathias Sirvent
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Martin Schmidt: Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
Denis Aßmann: Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
Robert Burlacu: Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
Jesco Humpola: Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany
Imke Joormann: Institut für Mathematische Optimierung, Technische Universität Braunschweig, Universitätsplatz 2, 38106 Braunschweig, Germany
Nikolaos Kanelakis: School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Thorsten Koch: Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany
Djamal Oucherif: Institut für Angewandte Mathematik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany
Marc E. Pfetsch: Department of Mathematics, Technische Universität Darmstadt, Dolivostr. 15, 64293 Darmstadt, Germany
Lars Schewe: Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
Robert Schwarz: Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany
Mathias Sirvent: Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany

Data, 2017, vol. 2, issue 4, 1-18

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.

Keywords: gas transport; networks; problem instances; mixed-integer nonlinear optimization; GasLib (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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