Optimal recovery sequencing for enhanced resilience and service restoration in transportation networks

Abstract

Critical infrastructure resilience has become a national priority for the U.S. Department of Homeland Security. Rapid and efficient restoration of service in damaged transportation networks is a key area of focus. The intent of this paper is to formulate a bi-level optimization model for network recovery and to demonstrate a solution approach for that optimization model. The lower-level problem involves solving for network flows, while the upper-level problem identifies the optimal recovery modes and sequences, using tools from the literature on multi-mode project scheduling problems. Application and advantages of this method are demonstrated through two examples.

Resilience management problem in ATM systems as a shortest path problem

“…References (Henry and Ramirez-Marquez, 2012) and (Vugrin et al, 2010) provide a survey about resilience measurement methodologies from a wide range of disciplines. Generally speaking, resilience metrics may be divided in (Vugrin et al, 2014):…”

“…The common framework underlying performance-based approaches employs a system performance metric F(,) as a basis for the resilience computation (Vugrin et al, 2014). This is a timedependent function, which represents the system delivery function or figure-of-merit.…”

“…Generic concept of disruption and recovery for resilience performance-based metrics(Vugrin et al, 2014).…”

“…In this way, we deal with resilience (and not robustness) actions(Vugrin et al, 2014).2 The stress typically implies a decrease in one or more KPIs.…”

A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures

“…Design of a resilient power projection network is a very important practical issue for strengthening the ability of strategic power projection. For response to hub failure, two strategies are usually adopted which include reactive (e.g., repairing [11] and rescheduling [12,13]) and proactive strategies (e.g., adjusting the network hub location strategy [14,15] and protecting key hubs [16][17][18][19]). It is sensible to take into account the impact of hub random disruptions in advance for designing a more resilient network.…”