Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Emergency department design evaluation and optimization using discrete event simulation
The proposed research would help any architect/owner decide the number of rooms/ cubicles for each sub-department of the ED, as well as have an estimated price for the ED, in order to optimally serve patients entering the ED with a known arrival rate. A thorough literature review was undertaken to c...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
AUC Knowledge Fountain
2012
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | The proposed research would help any architect/owner decide the number of rooms/ cubicles for each sub-department of the ED, as well as have an estimated price for the ED, in order to optimally serve patients entering the ED with a known arrival rate. A thorough literature review was undertaken to collect data concerning the application of decision support tools for minimizing patient waiting times and maximizing the utilization rate in health care systems. Interviews were made with hospital managers in order to verify process flow, waiting times, activity durations, and resources. In addition, several floor plans of EDs have been studied in order to assure the logical flow of the process. Based on the data collected and the several verifications, a discrete event simulation model was developed using ARENA software. This simulation model was then verified by building a similar model on different software, which was AnyLogic. The results proved the accuracy of the model. Twenty additional simulation runs were performed to be used for the regression analysis. The equations resulted from the regression analysis were used for the optimization model. A genetic algorithm was used for the purpose of obtaining optimized resource allocation for different arrival rates within a constrained budget, area, and patient waiting time in the system. |
|---|