Minimum cost network flow with interval capacities: The worst-case scenario

TL;DR

This paper investigates the complexity and structural properties of worst-case scenarios in minimum cost network flow problems with interval capacities, providing formulations, algorithms, and insights into paradoxical behaviors.

Contribution

It introduces a mixed-integer linear programming formulation and a pseudopolynomial algorithm for worst-case analysis, along with structural and paradoxical property characterizations.

Findings

Computing the worst optimal value is strongly NP-hard.

The extremal worst-case scenarios form a forest structure.

Increasing flow can paradoxically decrease worst-case costs in certain instances.

Abstract

We study the problem of determining the worst optimal value and characterizing the corresponding worst-case scenarios in minimum cost network flow problems with interval uncertainty in arc capacities. In this setting, each capacity can take any value within its specified lower and upper bounds. We prove that computing the worst optimal value is a strongly NP-hard problem and remains NP-hard even when restricted to series-parallel graphs. Further, we propose a mixed-integer linear programming formulation that computes the exact worst optimal value, as well as a pseudopolynomial-time algorithm designed for the special case of series-parallel graphs. We also examine the structural properties of the most extremal worst-case scenarios and show that the arcs whose capacities are not fixed at their interval bounds form a forest. This result establishes an upper bound on the number of such arcs, which we show to be tight by constructing a class of instances in which the bound is attained. Finally, we investigate the more-for-less paradox in minimum cost network flow problems with interval capacities, which occurs in instances where increasing the required flow leads to a decrease in the worst-case optimal cost. We provide a general characterization of this phenomenon using augmenting paths and establish a stronger characterization for complete graphs. In addition, we discuss the properties of the cost matrices immune against the paradox and prove that deciding whether a given cost matrix has this property is a strongly co-NP-hard problem.