Patient Scheduling
Patient scheduling in healthcare aims to optimize resource allocation and patient flow, balancing competing objectives like minimizing wait times, maximizing resource utilization, and accommodating patient preferences and urgency. Current research focuses on applying advanced optimization techniques, including constraint programming, stochastic local search, and bio-inspired algorithms (like genetic algorithms and wolf optimization), as well as machine learning models for prediction and decision support. These efforts aim to improve efficiency, reduce costs, and enhance patient outcomes across various settings, from surgical scheduling to radiotherapy appointments, by moving beyond manual processes and leveraging data-driven approaches. The ultimate goal is to develop robust and adaptable scheduling systems that can handle the inherent complexities and uncertainties of healthcare operations.