Memorias de investigación
Communications at congresses:
An Ant Colony System adaptation to deal with accessibility issues after a disaster
Year:2014
Research Areas
  • Engineering
Information
Abstract
One of the main problems relief teams face after a natural or man-made disaster is how to plan rural road repair work tasks to take maximum advantage of the limited available financial and human resources. Previous research focused on speeding up repair work or on selecting the location of health centers to minimize transport times for injured citizens. In spite of the good results, this research does not take into account another key factor: survivor accessibility to resources. In this paper we account for the accessibility issue, that is, we maximize the number of survivors that reach the nearest regional center (cities where economic and social activity is concentrated) in a minimum time by planning which rural roads should be repaired given the available financial and human resources. This is a combinatorial problem since the number of connections between cities and regional centers grows exponentially with the problem size, and exact methods are no good for achieving an optimum solution. In order to solve the problem we propose using an Ant Colony System adaptation, which is based on ants? foraging behavior. Ants stochastically build minimal paths to regional centers and decide if damaged roads are repaired on the basis of pheromone levels, accessibility heuristic information and the available budget. The proposed algorithm is illustrated by means of an example regarding the 2010 Haiti earthquake, and its performance is compared with another metaheuristic, GRASP.
International
Si
Congress
International Conference on Operations Research
960
Place
Aachen, Alemania
Reviewers
Si
ISBN/ISSN
978-1-4799-6773-5
Start Date
02/09/2014
End Date
05/09/2014
From page
116
To page
116
Business Analytics and Optimzation
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial