Operational healthcare management faces growing pressure to develop efficient operating room scheduling systems because these systems require optimal resource management, minimum patient waiting times, and reduced operational expenses. Scheduling problems have traditionally been solved using mixed integer programming (MIP), but these methods have become computationally inefficient when dealing with large complex scheduling scenarios. Exact scheduling methods lose their practical value in hospital operating room planning as the problem complexity rapidly increases according to the number of available operating rooms and scheduled surgeries. The necessity for adaptable intelligent scheduling methods emerges because organisations require methods that maintain excellent solution quality along with swift execution and cost-effective resources. The Bat Algorithm (BA) techniques within swarm intelligence demonstrate successful potential to solve challenging combinatorial issues through natural optimisation methods. This paper uses five Bat Algorithm optimisations to resolve operating room scheduling by reducing makespans. The algorithms examined are the Modified Bat Algorithm (MBA), Chaotic Bat Algorithm (CBA), Discrete Bat Algorithm (DBA), Multi-Objective Bat Algorithm (MOBA), and Binary Bat Algorithm (BBA). For this analysis, two distributions are the Pearson and Fisher distributions. We perform several Operating room scheduling studies to assess the effectiveness and efficiency of these five algorithms on different distributions and compare the results. The findings indicate that all five improved the solution of Operating room scheduling, but the Chaotic Bat Algorithm (CBA) with Fisher's distribution became the best solution for the given scenario.