The Robust Model of Continuous Transportation Network Design Problem with Demand and Cost Uncertainties

AbstractIn this paper, the continuous transportation network design problem with demand and cost uncertainties is studied while the generated trip between each origin-destination pair is deterministic. Propose a bi-level uncertain model with robust optimizations. In the upper-level problem, the costs of expansion are uncertain by adding random variables to the cost function coefficients. Every random variable belongs to [0,1]. In the lower-level problem, the demand between each origin-destination pair is uncertain and belongs to a bounded interval and an ellipsoid. Then apply a genetic algorithm combined with the Frank-Wolfe algorithm to solve the model. Numerical examples suggest that the robust model with uncertain data has the advantage of operability and stability over the deterministic model