5249
2015
eng
53
78
1
3
article
0
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A primal heuristic for optimizing the topology of gas networks based on dual information
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.
EURO Journal on Computational Optimization
10.1007/s13675-014-0029-0
yes
urn:nbn:de:0297-zib-43110
Jesco Humpola
Jesco Humpola
Thomas Lehmann
Armin Fügenschuh
Mathematical Optimization
Mathematical Optimization Methods
MODAL-GasLab
ForNe
MODAL-Gesamt