Efficient Optimization of Post-Disaster Reconstruction of Transportation Networks

Abstract

Catastrophes, such as hurricanes, earthquakes, and tsunamis often cause large-scale damage to transportation systems. In the aftermath of these disasters, there is a present challenge to quickly analyze various reconstruction plans and assess their impacts on restoring transportation services. This paper presents a new methodology for optimizing post-disaster reconstruction plans for transportation networks with superior computational efficiency employing mixed-integer linear programming (MILP). The model is capable of optimizing transportation recovery projects prioritization and contractors assignment in order to simultaneously: (1) accelerate networks recovery; and (2) minimize public expenditures. The full methodology is presented in two companion publications, where the focus of this paper is to propose new methods for (1) decomposing traffic analysis; (2) assessing the traffic and cost performance of reconstruction plans; (3) reducing the massive solution search space; and (4) phasing the use of mixed-integer linear programming to optimize the problem. An illustrative example is presented throughout the paper to demonstrate the implementation phases.