Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization
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Date
2022-12Type
- Journal Article
Abstract
One of the main challenges in the postdisaster management of large transportation networks involves the determination of the priority and the level of service recovery for each damaged asset in the network. Presently, the application of metaheuristic algorithms in developing restoration programs is receiving increasing attention. These algorithms determine a good solution to minimize the consequences of extreme events on the network of study in a relatively short period of time. This paper investigates the suitability of a discrete particle swarm optimization (DPSO) algorithm in finding a good solution to a restoration model developed for minimizing the overall direct and indirect costs of postdisaster restorative interventions. This model can consider constraints and limitations on the available budget, work groups and equipment, as well as different levels and speeds of service recovery for assets per damage state, and the changes in the traffic flow as the restorative interventions are executed. Moreover, the model has the capacity to process complex networks; hence, it can be implemented in real-world postdisaster decision making related to the development of restoration programs. The results suggest that the DPSO algorithm is a suitable choice of optimization algorithm in situations where the number of damaged objects is medium to large. Show more
Publication status
publishedExternal links
Journal / series
Journal of Infrastructure SystemsVolume
Pages / Article No.
Publisher
American Society of Civil EngineersSubject
Postdisaster decision making; Optimal restoration program; Discrete particle swarm optimization (DPSO); Roadnetwork resilience; Combinatorial optimizationOrganisational unit
03859 - Adey, Bryan T. / Adey, Bryan T.
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
Funding
769373 - Future proofing strategies FOr RESilient transport networks against Ectreme Events (EC)
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