Improved optimization models for potential-driven network flow problems via ASTS orientations

The class of potential-driven network flow problems provides important models for a range of infrastructure networks that lead to hard-to-solve MINLPs in real-world applications. On large-scale meshed networks the relaxations usually 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 and to yield significant performance improvements for an existing state-of-the-art MILP model for large-scale gas networks.

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ZIB-Report 19-58, Zuse Institute Berlin, Takustra├če 7 14195 Berlin, December 2019, updated July 2020.

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