Abstract
We study dynamic network flows with uncertain input data under a robust optimization perspective. In the dynamic maximum flow problem, the goal is to maximize the flow reaching the sink within a given time horizon T, while flow requires a certain travel time to traverse an edge. In our setting, we account for uncertain travel times of flow. We investigate maximum flows over time under the assumption that at most \(\varGamma \) travel times may be prolonged simultaneously due to delay. We develop and study a mathematical model for this problem. As the dynamic robust flow problem generalizes the static version, it is NP-hard to compute an optimal flow. However, our dynamic version is considerably more complex than the static version. We show that it is NP-hard to verify feasibility of a given candidate solution. Furthermore, we investigate temporally repeated flows and show that in contrast to the non-robust case (that is, without uncertainties) they no longer provide optimal solutions for the robust problem, but rather yield a worst case optimality gap of at least T. We finally show that the optimality gap is at most \(O(\eta k \log T)\), where \(\eta \) and k are newly introduced instance characteristics and provide a matching lower bound instance with optimality gap \(\varOmega (\log T)\) and \(\eta = k = 1\). The results obtained in this paper yield a first step towards understanding robust dynamic flow problems with uncertain travel times.







Similar content being viewed by others
References
Aneja, Y.P., Chandrasekaran, R., Nair, K.: Maximizing residual flow under an arc destruction. Networks 38(4), 194–198 (2001)
Aronson, J.E.: A survey of dynamic network flows. Ann. Oper. Res. 20(1), 1–66 (1989)
Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton (2009)
Bertsimas, D., Nasrabadi, E., Stiller, S.: Robust and adaptive network flows. Oper. Res. 61(5), 1218–1242 (2013)
Bertsimas, D., Sim, M.: Robust discrete optimization and network flows. Math. Program. Ser. B 98, 49–71 (2003)
Dilworth, R.P.: A decomposition theorem for partially ordered sets. Ann. Math. 51, 161–166 (1950)
Disser, Y., Matuschke, J.: The complexity of computing a robust flow (2017). arXiv:1704.08241
Du, D., Chandrasekaran, R.: The maximum residual flow problem: NP-hardness with two-arc destruction. Networks 50(3), 181–182 (2007)
Ford Jr., L.R., Fulkerson, D.R.: Constructing maximal dynamic flows from static flows. Oper. Res. 6(3), 419–433 (1958)
Fortune, S., Hopcroft, J., Wyllie, J.: The directed subgraph homeomorphism problem. Theoret. Comput. Sci. 10(2), 111–121 (1980)
Grötschel, M., Lovász, L., Schrijver, A.: The ellipsoid method and its consequences in combinatorial optimization. Combinatorica 1(2), 169–197 (1981)
Gupta, U.I., Lee, D.T., Leung, J.T.: Efficient algorithms for interval graphs and circular-arc graphs. Networks 12(4), 459–467 (1982)
Karp, R.M.: Reducibility among combinatorial problems. In: Complexity of Computer Computations, pp. 85–103. Springer, Berlin (1972)
Koch, R., Nasrabadi, E., Skutella, M.: Continuous and discrete flows over time. A general model based on measure theory. Math. Methods Oper. Res. 73(3), 301–337 (2011)
Koch, T., Hiller, B., Pfetsch, M.E., Schewe, L.: Evaluating Gas Network Capacities. SIAM, Philadelphia (2015)
Köhler, E., Skutella, M.: Flows over time with load-dependent transit times. SIAM J. Optim. 15(4), 1185–1202 (2005)
Matuschke, J., McCormick, T.S., Oriolo, G., Peis, B., Skutella, M.: http://materials.dagstuhl.de/files/15/15412/15412.JannikMatuschke.ExtendedAbstract.pdf (2015)
Skutella, M.: An introduction to network flows over time. In: Cook, W.J., Lovász, L., Vygen, J. (eds.) Research Trends in Combinatorial Optimization, pp. 451–482. Springer, Berlin (2009)
Wood, R.K.: Deterministic network interdiction. Math. Comput. Model. 17(2), 1–18 (1993)
Acknowledgements
We thank the reviewers for their very careful reading of the manuscript and their valuable comments. We thank the DFG for their support within Project B06 in CRC TRR 154.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gottschalk, C., Koster, A.M.C.A., Liers, F. et al. Robust flows over time: models and complexity results. Math. Program. 171, 55–85 (2018). https://doi.org/10.1007/s10107-017-1170-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10107-017-1170-3