@article{SchmidtAssmannBurlacuetal.2017, author = {Schmidt, Martin and Assmann, Denis and Burlacu, Robert and Humpola, Jesco and Joormann, Imke and Kanelakis, Nikolaos and Koch, Thorsten and Oucherif, Djamal and Pfetsch, Marc and Schewe, Lars and Schwarz, Robert and Sirvent, Matthias}, title = {GasLib - A Library of Gas Network Instances}, volume = {2}, journal = {Data}, number = {4}, doi = {10.3390/data2040040}, year = {2017}, language = {en} } @article{HumpolaLehmannFuegenschuh2015, author = {Humpola, Jesco and Lehmann, Thomas and F{\"u}genschuh, Armin}, title = {A primal heuristic for optimizing the topology of gas networks based on dual information}, volume = {3}, journal = {EURO Journal on Computational Optimization}, number = {1}, doi = {10.1007/s13675-014-0029-0}, pages = {53 -- 78}, year = {2015}, abstract = {We present a novel heuristic to identify feasible solutions of a mixed-integer nonlinear programming problem arising in natural gas transportation: the selection of new pipelines to enhance the network's capacity to a desired level in a cost-efficient way. We solve this problem in a linear programming based branch-and-cut approach, where we deal with the nonlinearities by linear outer approximation and spatial branching. At certain nodes of the branching tree, we compute a KKT point of a nonlinear relaxation. Based on the information from the KKT point we alter some of the binary variables in a locally promising way exploiting our problem-specific structure. On a test set of real-world instances, we are able to increase the chance of identifying feasible solutions by some order of magnitude compared to standard MINLP heuristics that are already built in the general-purpose MINLP solver SCIP.}, language = {en} }