An optimized deployment plan of ambulances for trauma patients

Emergency medical service (EMS) control centres should focus on strategically deploying ambulances to enable trauma patients to receive better care. The work proposed here aims to find an optimal deployment plan of ambulances for the existing base stations using the genetic algorithm (GA) based optimization component. The GA has been modified by incorporating a new proportion-based population seeding method for initializing the population. Considering a set of assumptions, the authors have applied the new strategy for allocating an optimal count of ambulances to 28 base stations in Delhi. The working environment of EMS that includes stochastic requests, travel time, and dynamic traffic conditions has been taken into account, and the optimization strategy has been implemented in a MATLAB environment. With the proposed work, the authors have been able to reduce the average response time (ART) by 6.7%. The simulation result has also demonstrated a comparison between GA and particle swarm optimization (PSO). In addition, some numerical experiments are performed to conclude the impact of different attributes on the value of ART.