Optimal Location, Patient Routing, and Capacity Decisions for Endoscopy Clinical Network in Western Ontario: A Simulation-based Optimization Approach
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Thousands of Canadians die or suffer from colorectal cancer (CRC) every year. Unawareness of risk factors and the lack of sufficient screening capacity contributes to these numbers. In Ontario, CRC death rates are high, therefore, the Ministry of Health and Cancer Care Ontario and Long-Term Care have launched a population-based provincial colorectal cancer screening program. Considering that it is 92% curable if detected early, it is crucial for people to have access to screening facilities for routine screening to avoid serious consequences. In this study, we develop a simulation-based optimization approach to find most favorable facility locations, along with the necessary number of staff, equipment, and dedicated rooms within each facility to provide three endoscopy screening services: Colonoscopy, Gastroscopy, and Flexible-Sigmoidoscopy. The model and its results may provide insights to policy makers in facilitating public access to endoscopy screening resources in Ontario. We developed a discrete-event simulation model to mimic the parallel processes within an endoscopy clinic in order to estimate the associated utilizations of resources and average waiting times of all patient groups. The simulation model is used to iteratively test the desired number of doctors, nurses, and rooms within the facility for a given demand rate. Then, we integrated the simulation model with a search-based approximate-optimization algorithm which searches different sets of facility locations to open as well as capacity levels to allocate in each location, and estimate the expected total cost. The aim of the algorithm is to provide a location capacity decision that minimizes the expected total cost given that expected waiting times are within acceptable limits.
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Najmaddin Akhundov (2015). Optimal Location, Patient Routing, and Capacity Decisions for Endoscopy Clinical Network in Western Ontario: A Simulation-based Optimization Approach. UWSpace. http://hdl.handle.net/10012/9391