Optimized Restoration Schedule for Disrupted Railroad Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 9
Abstract
A method is proposed for optimizing the restoration sequence of damaged components in a disrupted rail freight network. Given the network’s demand matrix and capacity settings, a mixed-integer linear program (MILP) model is developed for assigning capacitated freight flows and minimizing total hourly costs. The cumulative cost increment (excess) is computed for each examined restoration sequence in a disruption scenario, using the duration of each restoration step and minimized hourly costs in intermediate network states from the MILP. A simple genetic algorithm (GA) is applied for finding the restoration sequence that minimizes the excess after a disruption event. A numerical case with a small network and a disruption scenario is synthesized to demonstrate this method. The optimized restoration sequence and schedule found by the GA are proved globally optimal in this case by exhaustive enumeration. The GA’s effectiveness is further verified with three additional disruption scenarios. A sensitivity analysis shows that the minimized excess is more sensitive to capacities of damaged components when capacity levels of components or upper limits of travel time are lower. The minimized excess is highly sensitive to the demand level when freight flows are moderately undersaturated. It is also found that restorations of damaged nodes should have higher priorities at lower capacities of damaged nodes, higher capacities of damaged links, and higher unit costs of alternate shipments by trucks.
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Data Availability Statement
The data used in this study were synthesized by the authors and are available upon request. The model is specified in the paper. The Python code used for this paper is proprietary.
Acknowledgments
The authors are grateful to the Federal Railroad Administration, which funded most of the work presented in this paper through Contract 693JJ620C000001. They are also grateful to anonymous reviewers whose comments helped in significantly improving this paper.
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Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 9 • September 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 16, 2020
Accepted: Apr 1, 2021
Published online: Jul 14, 2021
Published in print: Sep 1, 2021
Discussion open until: Dec 14, 2021
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