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In North America, thousands of locomotives operating in the railroad network require various types of mechanical work every day. This work may be unscheduled, periodic, or on demand and could be conducted at either a fixed facility or a movable facility. Each facility is characterized by the type of work it can provide. The long-term infrastructure planning of these facilities is vital to the efficiency of the railroad. In this paper, we develop a large-scale mixed-integer mathematical model for infrastructure planning. The model integrates and optimizes decisions about (1) Locations, capabilities, and capacities of fixed facilities, (2) Home locations and routing plans of movable facilities, and (3) Assignments of locomotive work demands to facilities. We propose a decomposition-based heuristic solution framework consisting of several underlying algorithms to solve this large-scale optimization model. Computational results on numerous scenario studies using field data from the railroad industry show that the proposed model and algorithms are capable of providing solutions that are significantly superior to the current practice. The successful application of our methodology to real-world railroad planning has clearly demonstrated the substantial cost-saving benefits of our models on infrastructure planning solutions.