Efficient Analysis and Optimization of Reconstruction Plans for Damaged Transportation Networks Following Disasters

Planning for post-disaster reconstruction of damaged transportation networks is a complex and computationally expensive effort. Decision makers need to prioritize the use of limited resources to maximize societal benefit through accelerated traffic services restoration, which involves evaluating numerous recovery scenarios. This paper presents a new model that enables efficient analysis and optimization of the post-disaster reconstruction of transportation networks that is capable of identifying optimal reconstruction plans using reasonable computational overhead. The model employs a number of search space reduction techniques using mixed linear-integer programming and goal programming-inspired approaches in order to explore the large space of possible alternative solutions. Each of these solutions is comprised of a set of interdependent variables, including (1) contractors selection; and (2) the start dates of reconstruction projects and their constituent work packages. An application example is used to demonstrate the new model's superior computational performance compared to recent research developments.