Installation of Railroad Wayside Defect Detectors

Abstract

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.