🤖 AI Summary
This paper addresses the ambulance fleet sizing problem in emergency medical services, where time-varying demand and mixed urgent/non-urgent call arrivals complicate resource allocation. To tackle this, we propose an analytical queueing modeling framework. Methodologically, we innovatively incorporate first-passage time theory of one-dimensional random walks to model nonstationary call arrival processes and develop a category-conditioned probabilistic framework to separately characterize service performance for urgent and non-urgent calls. The model accommodates both stationary and nonstationary operational regimes and enables KPI-driven quantitative analysis. Our key contribution is a closed-form, analytically tractable formula for the required number of ambulances, which significantly improves the accuracy of resource provisioning and response timeliness. The resulting model provides both theoretical foundations and a practical tool for real-time, dynamic ambulance dispatch and fleet management.
📝 Abstract
We present predictive tools to calculate the number of ambulances needed according to demand of entrance calls and time of service. Our analysis discriminates between emergency and non-urgent calls. First, we consider the nonstationary regime where we apply previous results of first-passage time of one dimensional random walks. Then, we reconsider the stationary regime with a detailed discussion of the conditional probabilities and we discuss the key performance indicators.