In continuation of a set of types of problems in fleet management science, Rail and inter-modal transportation have several noteworthy approaches to solutions.

Several interesting algorithms have been proposed for the Rail and Intermodal problem space. This is a complex system composed by different transport networks, infrastructures, different transport means and operators, such as dryage operators, terminal operators, network operators and others. Intermodal means there are lots of decision makers who must work in a coordinated manner for the system to run smoothly. If intermodal transport is to be developed, it will require more decision-making support tools to assist the decision-makers and stakeholders.

When train operations are perturbed, a new conflict free timetable must be recomputed such that the deviation from the original must be minimized. This scheduling problem is modeled with an alternative graph formulation and a branch and bound algorithm is developed. Some approaches use an integrated framework which deals with signal layout optimization, train scheduling optimization at microscopic level, and others.

Heuristic approaches include a look-ahead greedy heuristic and a global neighborhood search algorithm, in terms of railway total train delay. Scheduling additional train services to be integrated into the current timetables is a problem that is modeled as a hybrid job shop scheduling techniques that operate upon a disjunctive graph model of trains.

One approach presented a two phased train set routing algorithm to cover a weekly train timetable with minimal working days of a minimal number of train sets. The first step involves relaxing maintenance requirements and obtaining minimum cost routes by solving the polynomial relaxation. Then maintenance-feasible routes are generated from the crossovers of the minimum cost routes. This pragmatic approach seems particularly effective for the high-speed railways system which is simpler, with fewer end stations and higher frequency of trains.

Corman et al addressed the problem of train conflict detection and resolution that became quite popular among traffic controllers. This approach proposed a family of techniques referred to as the Railway Optimization by Means of Alternative graphs and it involved effective rescheduling algorithms and local rerouting strategies in a tabu search scheme. A fast heuristic and a truncated branch and bound algorithm are alternate for computing train schedules within a short computation time. The effectiveness of using different neighborhood structures can be investigated for train rerouting. Tabu search is known to be faster than ROMA. Another approach proposed a train slot selection model based on multicommodity network flow concepts for determining freight train timetables for scheduling rail services along multiple interconnected routes. The model seeks to minimize operating costs incurred by carriers and delays incurred by shippers while ensuring that the schedules and demands are mutually consistent.