"The goal of this thesis is to assist a local public health department to plan for a mass medication closed Point of Dispensing (POD) in a large casino. The objective is to identify the best resource allocation for the stations in the POD in order to maximize the throughput considering uncertainty. The major unknown and uncertain factor affecting this critical decision is the traffic intensity. A Bayesian decision model is constructed to plan this closed POD site. […] The results show that the best resource allocation scenario is to allocate 6 nurses to the triage station, 9 casino staff to the registration station, 9 casino staff to the screening station and 12 nurses to the dispensing station. The ultimate goal of taking 3 minutes to process each person in the POD could not be achieved. In the recommended resource allocation scenario the minimum expected time per person is 4 minutes. In conclusion, the Bayesian decision analysis approaches used in this research can assist public health departments with determining the best resource allocation in POD stations. The models will also provide decision makers with insight about the traffic intensity of the system given the limited data available."
Copyright 2015 by Heba Mohamed Aly
North Carolina State University https://www.ncsu.edu/