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

Please always quote using this URN: urn:nbn:de:0297-zib-75347
  • 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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Metadaten
Author:Kai-Helge Becker, Benjamin HillerORCiD
Document Type:ZIB-Report
Tag:ASTS orientations; bipolar orientations; mixed-integer nonlinear programming; potential-driven network flows
MSC-Classification:90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
CCS-Classification:F. Theory of Computation
Date of first Publication:2019/12/02
Series (Serial Number):ZIB-Report (19-58)
ISSN:1438-0064
DOI:https://doi.org/10.12752/7534