Optimizing Hospital Operating Room Scheduling: A Comparative Analysis of Metaheuristic Algorithms for Job Shop Scheduling

This research presents a comprehensive and rigorous performance evaluation of four leading metaheuristic algorithms: Genetic Algorithm (GA), Tabu Search (TS), Simulated Annealing (SA), and Ant Colony Optimization (ACO) when applied to the Job Shop Scheduling Problem (JSSP), with a specific focus on Hospital Operating Room Scheduling. Effective operating room scheduling is crucial for optimizing resource utilization, minimizing patient waiting times, and reducing overall hospital operational costs. Given the inherent complexities of JSSP, such as resource constraints and task dependencies, metaheuristic algorithms provide promising solutions for achieving efficient schedules. By leveraging advanced performance profiling tools such as <chrono>, gperftools, and Valgrind’s Callgrind, this research meticulously measures algorithmic efficiency, evaluating critical performance metrics including execution time, cache misses, memory usage, and instructions executed. The findings provide a comprehensive understanding of the computational trade-offs associated with each algorithm, yielding valuable insights into their practical applications for hospital operating room scheduling. These insights form the basis for selecting the most effective metaheuristic algorithms customized to the specific demands of healthcare scheduling and optimization challenges.