Proceedings of the 21st international conference on Computers and industrial engineering
Practical genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulation with Arena
A nurse rostering system using constraint programming and redundant modeling
IEEE Transactions on Information Technology in Biomedicine
Hybrid optimization techniques for the workshift and rest assignment of nursing personnel
Artificial Intelligence in Medicine
A hybrid system for planning the development level of resort
Expert Systems with Applications: An International Journal
Winter Simulation Conference
A Fuzzy Temporal Rule-based System for handling the Nursing Process on mobile devices
Expert Systems with Applications: An International Journal
Simulation with data scarcity: developing a simulation model of a hospital emergency department
Proceedings of the Winter Simulation Conference
A simulation-based decision support system to model complex demand driven healthcare facilities
Proceedings of the Winter Simulation Conference
Improving the emergency department performance using simulation and mcdm methods
Proceedings of the Winter Simulation Conference
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This report shows how the quality of service at a hospital emergency department (ED) can be improved by utilizing simulation and a genetic algorithm (GA) to appropriately adjust nurses' schedules without hiring additional staff. The simulation model is developed to cover the complete flow for the patient through the ED. The GA is then applied to find a near-optimal nurse schedule based on minimizing the patients' queue time. The data for this research was collected from the ED of Show-Chwan Memorial Hospital in Central Taiwan. After computational analysis and comparisons, we found that by making appropriate adjustments to the nurses' schedules, the patients' queue time is shortened, thereby raising the quality of patient-care and patient-satisfaction.