An Optimized Ambulance Dispatching Solution for Rescuing Injures after Disaster

Abstract

In this work, we propose a Genetic Algorithm (GA) for effectively scheduling ambulances aftermath of a disaster. Given the limited number and capacity of ambulances, we aim to minimize the number of ambulance tours and the average time to take all injured people to a hospital. Both of these goals require that the total tour length of all routes should be minimized as well. This problem can be considered as an extension of the well-known Capacitated Vehicle Routing Problem (CVRP). We developed a Genetic Algorithm (GA) and tested using some of the CVRP benchmark files. For the possible number of injures at each location, we define three different scenarios. The proposed GA aims to minimize the tour lengths of the ambulances while respecting all real life constraints given in these scenarios. In order to evaluate the proposed GA, we also developed a rival method based on the Nearest Neighbor (NN) heuristic. The results of extensive simulation test NN heuristic.