A Hypercube Queuing Model Approach for the Location Optimization Problem of Emergency Vehicles for Large-Scale Study Areas

Emergency service systems provide essential services to people in need. Most of them have to operate under uncertainty and in complex environments. Their locations have to be chosen in a way that all or at least most of the incoming demand can be covered within a justifiable response time. In this paper, a hypercube queuing approach is presented that locates a high amount of emergency units within a large-scale study area. Due to the hypercube restrictions, computational times increase with the number of servers that are located. Therefore, an algorithm for the aggregation of demand areas is presented. To find an at least appropriate solution, a genetic algorithm is applied. It can be shown that computational times can be lowered significantly while the solution error is minimal. Furthermore, average response times for the emergency service system decrease with the location of additional servers in the study area.