Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization

One of the main challenges in postdisaster management large transportation networks involves determination priority and level service recovery for each damaged asset network. Presently, application metaheuristic algorithms developing restoration programs is receiving increasing attention. These determine a good solution to minimize consequences extreme events on network study relatively short period time. This paper investigates suitability discrete particle swarm optimization (DPSO) algorithm finding model developed minimizing overall direct indirect costs restorative interventions. can consider constraints limitations available budget, work groups equipment, as well different levels speeds assets per damage state, changes traffic flow interventions are executed. Moreover, has capacity process complex networks; hence, it be implemented real-world decision making related development programs. The results suggest that DPSO suitable choice situations where number objects medium large.