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After the deregulation of the Swedish railway industry, train operating companies compete for and on the same infrastructure. This makes the allocation of rail capacity a most delicate problem, and for a well-functioning railway system the allocation must be fair, efficient and functional. The capacity allocation tasks include e.g. constructing the yearly timetable and making track allocation plans for rail yards. The state of practice is that experienced planners construct the schedules manually with little or no decision support. However, as the planners are often faced with large combinatorial problems that are notoriously hard to solve there is a great potential in implementing optimising decision support systems. The research presented in this licentiate thesis aims at developing and examining mathematical models and methods that could be part of such support systems. The thesis focuses on two planning problems in particular, and the presented methods have been developed especially for the Swedish railway system. First of all, a model for optimising a train timetable with respect to robustness is presented. The model tries to increase the number of alternative meeting locations that can be used in a disturbed traffic situation and has an execution time of less than 5 minutes when solving the problem for the track section between Boden and Vännäs.                                                                                                                Secondly, the problem of generating efficient classification bowl schedules for shunting yards is examined. The aim is to find the track allocation that minimises the number of required shunting movements while still respecting all operational, physical and time constraints imposed by the yard.  Three optimisation models are presented, and simple planning rules are also investigated. The methods are tested on historic data from Hallsberg, the largest shunting yard in Sweden, and the results show that while the simple planning rules are not adequate for planning the classification bowl, two of the optimisation models consistently return an optimal solution within an acceptable execution time.