An Enhanced Genetic Algorithm With Clustering for Optimizing Mobile Stroke Unit Deployment

Mobile stroke units (MSUs), which are specialized ambulances equipped with a brain imaging device and staffed with trained healthcare personnel, have the potential to provide rapid on-site diagnosis and treatment for stroke patients. However, efficient access to prehospital stroke care requires optimizing the placement of MSUs. The MSU allocation problem has been previously solved using a traditional genetic algorithm that utilizes random starting solutions. The use of random starting solutions can, however, cause the algorithm to converge slowly. This can be especially problematic if the initial solutions are significantly far from the global optimum. To address this problem, we propose an enhanced genetic algorithm with clustering (EGAC), which is a time-efficient method to solve the MSU allocation problem by identifying the optimal locations of MSUs in a geographic region. By leveraging clustering, the EGAC provides diverse and comprehensive coverage, avoiding the pitfalls of starting with closely located and potentially less optimal solutions, thereby effectively steering and accelerating its convergence towards the optimal MSU placements. Our experimental results show that the EGAC significantly outperforms the traditional genetic algorithm, without cluster-based starting solutions, by achieving remarkably faster convergence toward the optimal solution for different number of MSUs to allocate. We validate the performance of the EGAC through qualitative and quantitative analyses.