Some more interesting algorithms for the Rail and Intermodal problem space, now follows.

There’s even an approach to do real-time management of a metro rail terminus. It 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 optimizing it to optimize the fleet size and freight car allocation under uncertainty demands.