Ant colony optimization and parameter selection for evacuating the mobility-challenged in a short-notice disaster
Heath, Susan K.
Tan, Yong Huy Ronny
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We address an Over-burdened Vehicle Routing Problem (OBVRP) model developed for evacuating mobility-challenged people who cannot self-evacuate before a short-notice disaster. Hurricane Katrina made it very clear that such evacuation assistance is needed. This OBVRP generates routes for multiple heterogeneous vehicles to evacuate people with differing transportation needs to minimize the number of people that cannot be evacuated due to limited time and vehicles. We improve upon a previously developed Ant Colony Optimization (ACO) algorithm through formal experimentation of the parameter settings using a Nearly-Orthogonal Latin Hypercube experimental design, and then constructing regression models using the results to determine the best parameter combinations. Our results show that the parameter settings can significantly affect the performance of the ACO algorithm and also show extensive interactions between the parameters. We compare our results to existing literature on ACO parameter selection and interactions, providing a great deal of insight not seen in prior literature.
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