Determination of Near‐Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing

Abstract

Disruptive events, such as earthquakes, floods, and landslides, may disrupt the service provided by transportation networks on a vast scale, as their occurrence is likely to cause multiple objects to fail simultaneously. The restoration program following a disruptive event should restore service as much, and as fast, as possible. The estimation of risk due to natural hazards must take into consideration the resilience of the network, which requires estimating the restoration program as accurately as possible. In this article, a restoration model using simulated annealing is formulated to determine near‐optimal restoration programs following the occurrence of hazard events. The objective function of the model is to minimize the costs, taking into consideration the direct costs of executing the physical interventions, and the indirect costs that are being incurred due to the inadequate service being provided by the network. The constraints of the model are annual and total budget constraints, annual and total resource constraints, and the specification of the number and type of interventions to be executed within a given time period. The restoration model is demonstrated by using it to determine the near‐optimal restoration program for an example road network in Switzerland following the occurrence of an extreme flood event. The strengths and weaknesses of the restoration model are discussed, and an outlook for future work is given.

Resilience-Based Restoration Model for Supply Chain Networks

“…In this section, we mainly review the approaches that have been adopted to solve this problem. The existing approaches can be classified into two groups, i.e., the mathematical programming approach [19,20] and the simulation-based approach [21,22]. The approach employed in this research falls in the first group.…”

Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks

“…Simulated annealing is a popular optimization algorithm and has shown good performance when used to solve optimization problems related to civil engineering (Chen & Shahandashti, 2009;Hackl, Adey, & Lethanh, 2018;Nayak & Turnquist, 2016;Paya, Yepes, González-Vidosa, & Hospitaler, 2008;Zeferino, Antunes, & Cunha, 2009). One of the strong attributes of metaheuristic-based optimizers, such as simulated annealing is that they do not make any strong assumptions regarding the objective function, such as its convexity or continuity.…”

Estimation of traffic flow changes using networks in networks approaches

“…To prevent such situations, scientists and engineers are working on the implementation of resilient systems capable of withstanding failures, natural hazards and human-made disruptions. Part of the research deals with the quantification of network-related risks, including the modelling of traffic flows after multiple link failures (Erath 2011;Hackl et al 2018a;Hackl et al 2018b).…”

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