Topological Approach for Optimizing Railroad Freight Network Restoration after Disruptions
Fei Wu, Paul Schonfeld, Bilal M. Ayyub, Myungseob Kim
Abstract
A topology-based method is proposed for optimizing the restoration sequence of damaged components in a disrupted rail freight network. Formulated as a demand-weighted average of reciprocal shortest path lengths, network efficiency is used as an indicator of overall connectivity for origin–destination (OD) pairs having freight demand. With the fixed demand matrix, a given network configuration, and a given disruption scenario, the cumulative loss of network efficiency during the restoration process is computed for each evaluated restoration sequence, using network efficiency values in intermediate network states as well as the duration of each restoration phase (i.e., restoration plus access time). This cumulative loss is treated as a measure of post-disruption network resilience, and is minimized with a simple genetic algorithm (GA) that finds the corresponding optimized restoration sequence, which also determines the optimized restoration schedule. The proposed method is demonstrated in a synthesized numerical case of a small network and a disruption scenario. The GA can find the globally optimal restoration sequence relatively fast, with its effectiveness further verified through exhaustive enumeration for three additional disruption scenarios. Sensitivity analysis results indicate that higher topological centrality and freight throughput of damaged nodes or disruption-induced isolation of some nodes are responsible for higher minimized loss of cumulative efficiency. The optimized restoration sequence tends to prioritize nodes and adjacent links with relatively high freight throughput in normal operation.