Optimal resource management in hospitals, particularly in critical departments such as operating rooms, plays a pivotal role in enhancing both economic performance and service quality. One of the key challenges in this context is the efficient scheduling of surgeries, considering resource constraints and uncertainty in surgery durations. This study focuses on developing a mathematical model for daily operating room scheduling under uncertainty whose decision variables include patient-to-operating room assignments, binary scheduling flags, start times per patient per scenario, staff assignments to operating rooms, sequencing relations between surgeries, operating room opening flags, overtime and under-utilization amounts, and bed allocations. The primary objectives of the model include improving collaboration satisfaction between surgeons and surgical assistants, allocating surgery times based on patient urgency, and reducing overall costs. The main innovation of the proposed model lies in the simultaneous consideration of staff collaboration preferences, comprehensive cost management, and the inclusion of personnel with varying specialties. Moreover, by incorporating different scenarios for surgery durations and influential factors such as surgeon skill levels, the model provides more realistic and applicable outcomes. To solve the problem under uncertainty, a novel genetic algorithm is presented. This algorithm features an innovative and comprehensive chromosome design and a customized crossover operator tailored to the specific characteristics of the problem. For instances where the optimal solution is known, the proposed algorithm achieves an average error margin of 1.26%. Additionally, for large-scale instances where commercial solvers fail to provide solutions within an hour, the performance and speed of the algorithm have been thoroughly evaluated. Computationally, the tailored chromosome design, repair mechanisms, and order-preserving crossover were critical to maintaining feasibility and producing high-quality sequences. Managerial metrics derived from GA outputs indicate reductions in operating room idle time and overtime exposure as well as fewer specialty-mismatch penalties and unscheduled patients compared to baseline schedules used in comparable literature instances. For practice, the proposed method offers hospital managers a flexible decision-support approach to produce realistic daily schedules under uncertain durations while balancing human factors and cost.