The Application of Multifunction Stochastic Service Systems in Allocating Ambulances to an Urban Area

Abstract

Alternative policies for the allocation and distribution of ambulances are studied for the city of Detroit, Michigan. Data on emergency occurrence and service processes were collected and analyzed, and these data analyses are utilized to model the ambulance system as a multifunction stochastic service system with semi-Markov arrivals and state-dependent server selection. Numerical results are developed predicting the performance of both single function recovery systems and dual function police-ambulance systems under alternative operating policies.

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