Risk and resilience-based restoration optimization of transportation infrastructures under uncertainty.

Area of Science:

• Transportation Engineering
• Operations Research
• Risk Management

Background:

• Disruptive events severely impact transportation infrastructure functionality and incur significant financial losses.
• Optimal restoration planning is crucial for enhancing transportation system resilience and mitigating risks.
• Uncertainties inherent in restoration activities necessitate advanced risk assessment methodologies.

Purpose of the Study:

• To develop a stochastic bilevel optimization model for transportation infrastructure restoration planning under uncertainty.
• To incorporate a conditional value at risk with regret (CVaR-R) measure for robust risk assessment.
• To propose an integrated algorithmic framework for solving the complex stochastic optimization problem.

Main Methods:

• Formulation of a bilevel optimization model using total travel time as the resilience measure.
• Representation of uncertainty through scenario generation using the Latin hypercube technique.
• Development of an integrated framework combining a genetic algorithm and the Frank-Wolfe algorithm for stochastic model solution.

Main Results:

• The proposed stochastic bilevel programming model effectively addresses uncertainties in transportation restoration.
• The integrated genetic-Frank-Wolfe algorithm demonstrates efficient performance in solving the stochastic model.
• Numerical experiments validate the model's properties and the algorithm's effectiveness.

Conclusions:

• The developed methodology offers a robust approach to transportation restoration optimization under uncertainty.
• The study provides valuable insights for emergency decision-making in transportation infrastructure management.
• The CVaR-R measure enhances risk assessment by considering worst-case scenarios in restoration planning.