The process used to determine Emergency
Medical Services (EMS) standby locations within a community varies greatly
from locality to locality. Site determination methods usually incorporate
two categories of consideration: strategic and tactical. The objective
of this research is to develop a technique to strategically locate and
allocate vehicles in the context of siting Multilevel EMS vehicles in
an urban environment in which the stochastic nature of the urban environment
is taken into account explicitly. The Queueing Maximum Availability
Location (Q-MALP) model is extened to locate two types of servers, i.e.,
Basic Life Support (BLS) and Advanced Life Support (ALS), henceforth
known as the MQ-MALP model.
The development of the model includes the
randomness of server availability and of travel times. Two approaches
for the treatment of random travel times are presented. The first incorporates
a measure of uncertainty for travel times, i.e., the probability measure
into the MQ-MALP optimization model, while the second uses a Monte Carlo
simulation of travel times as inputs into the MQ-MALP optimization model
with a heuristic method developed to site the vehicles.
These models are applied to two test problems:
a 33-node census tract representation of Austin, Texas and a 55-node
test case. The implication of these models for the EMS system design
are discussed as well as the limitations of the modelling approach
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