As the economies of the world become more interrelated and Supply Chains are globalizing, the need arises to create efficient transportation network. This reality in conjunction with conservation of fuel and environmental friendliness gives rise to the research of Efficient Intermodal Transportation System. In particular, the underutilization of railroads in the United States motivates us to research the development of optimal procedures in the transportation of containers in a rail network. With this thesis we search for a cost, time and capacity effective algorithm for solving transportation problem in a graph of intermodal centers (IMC\u27s). We consider discrete model of the real time dynamic situation when all the arcs of the input graph can be affected by changes in their costs, the transportation means have limited and different container capacities at each IMC, and all the nodes (IMC\u27s) can be visited more than once either by different transport means or at different time. This is more general and real situation than the ones considered in the literature so far. The resulting optimization problem is computational intractable (NP-hard), which creates the necessity to develop, implement and test efficient heuristic optimization techniques. We will use Shortest Path Problem (SPP) as the basis for the development of three heuristics. Because of the nature of the problem and application, shortest path procedures provide a very flexible and computationally efficient technique for our model. We will compare the three heuristics with the optimal solution for small size problems for which we could find optimality. Furthermore, we will demonstrate that one of the heuristics perform very well when the fixed costs of running transportation modes is the dominant aspect of the cost structure
