Algorithms for computing fractional solutions to the quickest transshipment problem have been significantly improved since Hoppe and Tardos first solved the problem in strongly polynomial time. For integral solutions, runtime improvements are limited to general progress on submodular function minimization, which is an integral part of Hoppe and Tardos' algorithm. Yet, no structural improvements on their algorithm itself have been proposed. We replace two central subroutines in the algorithm with methods that require vastly fewer minimizations of submodular functions. This improves the state-of-the-art runtime from $ \tilde{O}(m^4 k^{15}) $ down to $ \tilde{O}(m^2 k^5 + m^4 k^2) $, where $ k $ is the number of terminals and $ m $ is the number of arcs.

ABSTRACT Peak cost is a novel objective for flows over time that describes the amount of workforce necessary to run a system. We focus on minimizing peak costs in the context of maximum temporally repeated flows and formulate the corresponding MPC‐MTRF problem. First, we discuss the limitations that emerge when restricting the solution space to integral temporally repeated flows, which is motivated by practical applications. We show that, in general, MPC‐MTRF has an integrality gap of and an arbitrarily bad approximation ratio compared to general flows over time. We proceed with a complexity analysis for MPC‐MTRF and show that both the decision version and the optimization version of integral MPC‐MTRF are strongly ‐hard, even under strong restrictions. On the positive side, we identify two special cases that are solvable in polynomial time: unit‐cost series‐parallel networks and networks with time horizon at least twice as long as the longest path in the network with respect to the transit time. Moreover, in both cases, we provide an explicit algorithm that constructs an integral optimal solution.