In emergency medical services (EMS), response time is a key factor that shows the performance of the system. This factor is composed of two parts including activation time and travel time. The activation time of an emergency call is the time a call center spends on taking an incoming call for EMS and decides whether an ambulance should be dispatched or the emergency can be resolved by giving some medical advices. Having a good number of call center operators including call takers and dispatchers can help to reduce this time and thus the response time. However, this alone may not be enough to have a reasonable cycle time. In fact, having idle ambulances located in right places when emergency calls occur is an influential factor to reduce the travel time and thus have an optimal response time and save people's lives. Although many researchers have studied improving either the activation time or the travel time, to the best of our knowledge, there exists no study focusing on both. Motivated by this gap, in this study, a framework is proposed which uses queuing theory and location analysis to address both activation and travel times. In this regard, first, queuing theory is applied to evaluate the performance of an EMS call center and reduce the average activation time. Then, three mathematical models including mobile stations, MEXCLP and MECRP are applied to locate ambulances such that the average travel time is minimized. To shed light on the merits of using queuing theory and location models, computational results based on the real-world data from Tehran EMS Center are provided. The proposed methodology can reduce the average response time by approximately four minutes and increase the average coverage by 35%. This work is an example of the tremendous positive impact that the application of queuing theory and mathematical modeling can have. The proposed framework can significantly decrease the cycle time and increase the ambulance coverage level. The findings of this study help the policy makers of EMS centers with evaluating the performance of their systems and determining the optimal number of operators and locations of ambulances in a way that enhances the quality of emergency services.