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.
Installation of Railroad Wayside Defect Detectors
Multiperiod Design
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2261 , 1 ; 148-154
2011-01-01
Article (Journal)
Electronic Resource
English
Installation of Railroad Wayside Defect Detectors: Multiperiod Design
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