Abstract
Abstract. The successful conduct of a rescue mission in urban areas is directly related to the timely deployment of equipment and personnel to the incident location which justifies the quest for optimum path selection for emergency purposes. In this study, it is attempted to use Ant Colony Optimization (ACO) to find the optimum paths between fire stations and incident locations. It is also attempted to build up an evaluation tool using ACO to detect critical road segments that the overall accessibility to fire station services throughout the urban area is constituted upon their excellent functionality. Therefore, an ACO solution is designed to find optimum paths between the fire station and some randomly distributed incident locations. Regarding different variants of ACO, the algorithm enjoys the Simple Ant Colony Optimization deployment strategy combined with Ant Algorithm Transition rules. Iteration best pheromone updating is also used as the pheromone reinforcement strategy. The cost function used to optimize the path considers the shortest Euclidean distance on the network. The results explicitly state that the proposed method is successful to create the optimum path in 95.45 percent of all times, compared to Dijkstra deterministic approaches. Moreover, the pheromone map as an indicator of the criticality of road elements is generated and discussed. Visual inspection shows that the pheromone map is verified as the road criticality map concerning fire station access to the region and therefore pre-emptive measures can be defined by analyzing the generated pheromone map.
Highlights
Optimum path selection for rescue missions is believed to be the matter of life and death when every moment is vital to save human lives and to prevent the expansion of disaster, especially in urban areas
The aggregate of all studies in the field of the optimum path selection and disaster management justifies the inspection of Ant Colony Optimization (ACO) solutions for the rescue missions
The network of the roads Fire station and incident locations are depicted in figure 2
Summary
Optimum path selection for rescue missions is believed to be the matter of life and death when every moment is vital to save human lives and to prevent the expansion of disaster, especially in urban areas. Syarif et al (2018) proposed a solution for the shortest path problem using genetic algorithms. Genetic Algorithm, a pioneering evolutionary optimization algorithm, has been studied to fit the shortest path problem. As one of the pioneering studies, ACO is applied for routing and road network balancing (Sim and Sun 2003) In this survey, three major ACO methods were compared to each other, and the stagnation problem was discussed. The aggregate of all studies in the field of the optimum path selection and disaster management justifies the inspection of ACO solutions for the rescue missions. In this paper reviewing ACO basics, the proposed method for optimum path generation is defined and compared to the Dijkstra solution. The proposed method is tested using case study data
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