This paper investigates potential configuration challenges in the development of optimization-based computational re-scheduling support for railway traffic networks. The paper presents results from an experimental study on how the characteristics of different situations and the network influence the problem formulation and the resulting re-scheduling solutions. Two alternative objective functions are applied: a) Minimization of the delays at the end stations which exceed three minutes and b) minimization of delays larger than three minutes at intermediary commercial stops and at end stations. The study focuses on the congested, single-tracked Iron Ore line located in Northern Sweden and partially Norway. A combinatorial optimization model adapted to the special restrictions of this line is applied and solved using commercial optimization software. 20 different disturbance scenarios are solved and the resulting re-scheduling solutions are analyzed numerically and visually in order to better understand their practical impact. The results show that the two alternative, but similar, objective functions result in structurally, quite different re-scheduling solutions. The results also show that the selected objective functions have some flaws when it comes to scheduling trains that are ahead of their schedule by early departure, or by having a lot of margin time due to waiting time in meeting/passing locations. These early trains are not always “pushed” forward unless the objective function promotes that in some way. All scenarios were solved to optimality within 1 minute or less, which indicates that commercial solvers can handle practical problems of a relevant size for this type of setting.