TY - JOUR
T1 - Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization
AU - Moghtadernejad, Saviz
AU - Adey, Bryan Tyrone
AU - Hackl, Jürgen
N1 - Funding Information:
This work has received funding from the European’s Union Horizon 2020 research and innovation program under the Grant Agreement No. 769373 (FORESEE project), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Fonds de recherche du Québec - Nature et Technologie (FRQNT).
Publisher Copyright:
© 2022 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Combinatorial optimization
KW - Discrete particle swarm optimization (DPSO)
KW - Optimal restoration program
KW - Postdisaster decision making
KW - Road network resilience
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U2 - 10.1061/(ASCE)IS.1943-555X.0000725
DO - 10.1061/(ASCE)IS.1943-555X.0000725
M3 - Article
AN - SCOPUS:85139868511
SN - 1076-0342
VL - 28
JO - Journal of Infrastructure Systems
JF - Journal of Infrastructure Systems
IS - 4
M1 - 04022039
ER -