A dynamic operation room scheduling DORS strategy based on explainable AI and fuzzy interface engine

Abstract Poor surgical scheduling causes major problems in hospital operating rooms, such as long patient wait times, underutilized operating rooms, and high costs. Existing scheduling approaches, which are static or less adaptable, fail to handle real-time unpredictability. To overcome these constraints, this study presents Dynamic Operation Room Scheduling (DORS), a new intraday surgical scheduling system. DORS uses a two-layered architecture: (1) Explainable AI for feature selection that is based on critical scheduling criteria such as Round Robin, and (2) a dynamic scheduling system that includes a Receiving Module, a Checking Module for patient prioritization, and a Scheduling Module provided by a Fuzzy Interface Engine. This system allows for proactive schedule preparation and reactive modifications, making it possible to smoothly include unscheduled surgical operations. In comparison to traditional (FCFS, Round Robin) and optimization-based (genetic algorithm) methods. DORS dynamically modifies schedules to reduce average wait times (AWT), consistently outperforming other approaches by 120–560 min. DORS completes surgical operations more quickly (half of surgical operations in 255–725 min). In addition, DORS retains a modest runtime (45 ms) while increasing scheduling efficiency (98.6%). DORS also demonstrates strong stability, with low Relative Percentage Deviation (RPD) on high-demand days. Finally, DORS achieves the optimal blend of speed, efficiency, and responsiveness, making it the greatest choice for hospitals aiming to eliminate delays, optimize operating room usage, and effectively manage changing surgical needs.