New publication from Dr. Saviz Moghtadernejad in the Journal of Infrastructure Systems!
Prof Bryan Adey's Chair of Infrastructure Management congratulates Dr. Saviz Moghtadernejad on the publication of the article "Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization" in the Journal of Infrastructure Systems.
In this article, Dr. Saviz Moghtadernejad investigates the suitability of a discrete particle swarm optimization algorithm to minimize the overall direct and indirect costs of post-disaster restorative interventions. The restoration model contains 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. The model and DPSO algorithm show potential to be used to plan restoration interventions on complex networks in situations where the number of damaged objects is medium to large.
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.
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