A Metaheuristic Approach to Emergency Vehicle Dispatch and Routing

Accidents and emergency situations have been on a constant rise, especially during the COVID-19 pandemic. Typically, the emergency vehicle dispatch and routing problems involve various dynamic factors which make them very different from conventional vehicle routing problems. This paper presents a metaheuristic approach to emergency vehicle dispatch and routing. Dispatching aims at allotting and sending the nearby available vehicle to the location of emergency and routing deals with selecting the ideal route to reach the destination. The objective is to minimize incident response time and the total time travel for the vehicle from the dispatch point to the destination. This usually depends on the emergency service vehicle availability and other dynamic factors such as traffic, number of turns in the route, etc. Three different bio-inspired algorithms, namely, ant colony optimization, adaptive ACO and firefly algorithm are investigated. Performance evaluation shows that firefly algorithm outperforms the other algorithms in terms of cost, number of turns, and run time for the given data set. However, in case of larger datasets and multiple variables if involved, adaptive ACO gives better results but takes longer time.