TY - GEN A1 - Becker, Kai Helge A1 - Hiller, Benjamin T1 - Improved optimization models for potential-driven network flow problems via ASTS orientations N2 - The class of potential-driven network flow problems provides important models for a range of infrastructure networks. For real-world applications, they need to be combined with integer models for switching certain network elements, giving rise to hard-to-solve MINLPs. We observe that on large-scale real-world meshed networks the relaxations usu- ally employed are rather weak due to cycles in the network. To address this situation, we introduce the concept of ASTS orientations, a generalization of bipolar orientations, as a combinatorial relaxation of feasible solutions of potential-driven flow problems, study their structure, and show how they can be used to strengthen existing relaxations and thus provide improved optimization models. Our computational results indicate that ASTS orientations can be used to derive much stronger bounds on the flow variables than existing bound tightening methods. We also show that our proposed model extensions yield significant performance improvements for an existing state-of-the-art MILP model for large-scale gas networks. KW - mixed-integer nonlinear programming KW - potential-driven network flows KW - ASTS orientations KW - bipolar orientations Y1 - 2019 UR - https://opus4.kobv.de/opus4-matheon/frontdoor/index/index/docId/1432 UR - https://nbn-resolving.org/urn:nbn:de:0296-matheon-14324 ER -