Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 191, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2019.106557
Keywords
Resilience; Interdependencies; Transportation network; Electric power network; Resource allocation; Dynamic traffic assignment
Funding
- Center for Risk-based Community Resilience - National Institute of Standards and Technology (NIST) [70NANB15H044]
- United States Department of Transportation (Mountain Plains Consortium)
Ask authors/readers for more resources
Characterizing the interdependencies among highly interconnected critical infrastructure systems with adequate details is critical in devising cost-effective resilience improvement strategies. This study presents a bi-level, stochastic, and simulation-based decision-making framework for prioritizing mitigation and repair resources to maximize the expected resilience improvement of an interdependent traffic-electric power system under budgetary constraints. The upper level seeks to find the optimal resource allocation plan to maximize the expected attainable functionality gain. The lower level characterizes the functionalities of the traffic and electric power systems considering three types of interdependencies based on network flow analysis methods. The dynamic traffic assignment algorithm, rather than the static traffic assignment algorithm, is used in order to capture more realistic traffic dynamics in the congested urban roadway networks. Uncertainties in disruptions, traffic demands, and costs of mitigation and repair actions are also considered in the problem formulation. The problem is solved by the binary particle swarm optimization algorithm initialized with the knapsack-based heuristic, and the priority indices of disrupted components for mitigation and repair are then established based on the solutions. The proposed decision model is demonstrated using a portion of the traffic-electric power system in Galveston, Texas.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available