Emergency Medical Service Allocation in Response to

Large Scale Events

Gabriel Zayas-Cabán and Mark E. Lewis
School of Operations Research & Information Engineering
Cornell University
Ithaca, NY 14853

Matthew Olson and Samuel Schmitz
MITRE Corporation

7515 Colshire Drive

McLean, VA 22102-7539

In the event of a catastrophic or large scale event, demand for Emergency Medical Service

(EMS) vehicles will almost certainly overwhelm the available supply. In such cases, it

is necessary for cities to request aid (in the form of added capacity) from neighboring

municipalities in order to bring the affected region back to its day-to-day levels of operation.

In particular, we consider a region consisting of several cities, where each city is

in charge of managing its own EMS vehicles. We propose that a centralized or statewide

decision-maker coordinate the temporary transfer of resources (EMS vehicles) from cities

in the unaffected region into the cities in the affected region. The control of each city’s

EMS vehicles is modeled as a multi-server queueing system and classical results are used

to estimate the number of vehicles available at each city. We then develop a knapsack

model to guide the allocation of available vehicles from the donor area into the affected

one and a clearing system model to dynamically control the added resources.

As the dimension of the problem is large, a heuristic we call the buddy system is

proposed where cities are paired to form city groups. This reduces the size of the problem

enough to solve the knapsack problem. Within the city groups the clearing system model

is solved by Markov decision processes. The performance of our heuristic is compared

to several other reasonable heuristics via a detailed numerical study. Results show that

the buddy system exhibits significant cost and time savings, and is generally robust to

varying parameters.