TY - INPR A1 - Habeck, Oliver A1 - Pfetsch, Marc E. T1 - Combinatorial Acyclicity Models for Potential-based Flows N2 - Potential-based flows constitute a basic model to represent physical behavior in networks. Under natural assumptions, the flow in such networks must be acyclic. The goal of this paper is to exploit this property for the solution of corresponding optimization problems. To this end, we introduce several combinatorial models for acyclic flows, based on binary variables for flow directions. We compare these models and introduce a particular model that tries to capture acyclicity together with the supply/demand behavior. We analyze properties of this model, including variable fixing rules. Our computational results show that the usage of the corresponding constraints speeds up solution times by about a factor of 3 on average and a speed-up of a factor of almost 5 for the time to prove optimality. KW - Network Optimization KW - Potential networks KW - Potential-based flows KW - acyclic flows Y1 - 2020 ER - TY - JOUR A1 - Groß, Martin A1 - Marc E., Pfetsch A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Skutella, Martin T1 - Algorithmic Results for Potential-Based Flows: Easy and Hard Cases N2 - Potential-based flows are an extension of classical network flows in which the flow on an arc is determined by the difference of the potentials of its incident nodes. Such flows are unique and arise, for example, in energy networks. Two important algorithmic problems are to determine whether there exists a feasible flow and to maximize the flow between two designated nodes. We show that these problems can be solved for the single source and sink case by reducing the network to a single arc. However, if we additionally consider switches that allow to force the flow to 0 and decouple the potentials, these problems are NP-hard. Nevertheless, for particular series-parallel networks, one can use algorithms for the subset sum problem. Moreover, applying network presolving based on generalized series-parallel structures allows to significantly reduce the size of realistic energy networks. KW - Potential networks KW - Potential-based flows KW - Maximum flow problem KW - Series-parallel graphs KW - Network reduction Y1 - 2017 U6 - https://doi.org/10.1002/net.21865 VL - 73 IS - 3 SP - 303 EP - 324 ET - Networks ER -