Railroads use wayside inspection technologies to monitor the health of passing railcars. Because of resource constraints, an efficient and feasible installation plan should be integrated into a multiperiod decision–support framework to derive the maximum benefit over a long planning horizon. A large-scale network optimization framework was proposed to solve this problem. The problem was formulated into two equivalent mathematical models: a maximum coverage model and a K-median model. A set of solution algorithms including a greedy heuristic, a 1-interchange heuristic, and a Lagrangian relaxation method were developed for these models, and their computational performances were investigated. A numerical case study was developed to illustrate the use of these models in real-world settings, and insights into empirical applications of these models were drawn.