Several interesting algorithms have been proposed for the Rail and Intermodal problem space and this article continues the enumeration of those that were discussed earlier.

The real-time rail management of a terminus involves routing incoming trains through the station and scheduling their departures with the objective of optimizing punctuality and regularity of train service. The purpose is to develop an automated train traffic control system. The scheduling problem is modeled as a bicriteria job shop scheduling problem with additional constraints. The two objective functions in lexicographical order are the minimization of tardiness/earliness and the headway optimization.  The problem is solved in two steps.  A heuristic builds a feasible solution by considering the first objective function. Then the regularity is optimized. This works well for simulations of a terminus.

A simulation-based approach is also used for tactical locomotive fleet sizing. Their study shows the throughput increases with the number of locomotives up to a certain level. After that the congestion is caused by the movements of many locomotives in a capacity constrained rail network.

One correlation that seems to hold true is that the decisions on sizing a rail car fleet has a tremendous influence on utilizing that fleet. The optimum use of empty rail cars for demand response is one of the advantages to building a formulation and solving it to optimize the fleet size and freight car allocation under uncertainty demands.

This correlation has given rise to a model that formulates and solves for the optimum fleet size and freight car allocation. This model also provides rail network information such as yard capacity, unmet demands, and number of loaded and empty railcars at any given time and location. It is helpful to managers or decision makers of any train company for planning and management activities. A two-stage solution procedure for solving rail-car fleet.

Drayage operations are specific to rail. Intermodal transportation improves when these operations are considered. In the cities or urban areas, drayage suffers from random transit times. This makes fleet scheduling difficult. A dynamic optimization model could use real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Tasks can be flexible or well-defined. One application of this model was tried out on test data and then applied to a set of random drayage problems of varying sizes and characteristics.

Tactical design of scheduled service networks for transportation systems is one where different network coordinate and their coordination is critical to the success of the operations. For a given demand, a new model was proposed to determine departure times of the service such that the throughput time is minimized. This is the time that involves processing, inspection, move and queue times during demand. It could be considered a hybrid of some models discussed earlier such as the service network design that involves asset management and multiple fleet co-ordination to emphasize the explicit modeling of different vehicle fleets. Synchronization of collaborative networks and removal of border crossing operations have a significant impact on the throughput time for the freight.