In an emergency medical system, customer coverage is directly affected by how ambulances are allocated to customers and how they should be returned to stations. The purpose of this paper is to obtain the best return strategy for ambulances to maximize the expected coverage concerning a predefined dispatch policy. A hypercube queuing model is presented to maximize customers' coverage probability, in which locations of busy ambulances in each state are not known and approximated based on customer arrival rates. In the proposed repositioning model, only newly-available ambulances are moved to the free stations according to the predetermined return strategy. Some small- and medium-scale instances are solved exactly using the Gaussian elimination method. Multi-Verse Optimizer and Genetic algorithms are used in combination with the discrete-event simulation for solving large-sized problems. Moreover, real data from a case study are utilized to verify the performance of the proposed models.
