Bio-Inspired Metaheuristic Optimization for Elective Surgical Scheduling: A Real-World Two-Stage Flexible Flow-Shop Study

Elective surgical scheduling allocates operating rooms and specialized personnel under operational constraints, yet most metaheuristic evaluations rely on synthetic benchmarks. We study a two-stage flexible flow shop with independently assigned rooms for anesthesia preparation and surgery, calibrated with 23,865 valid records from a Chilean public hospital, and compare four population-based, bio-inspired methods: a Genetic Algorithm, discrete Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and a discrete Mantis Shrimp Optimization Algorithm. Across four 15–30-surgery instances and 400 replications, the particle swarm method achieves the lowest mean makespan among the metaheuristics for 20–30 surgeries, whereas the Secretary Bird Optimization Algorithm roughly halves the number of patients incurring inter-stage waiting relative to that method (p<0.001) and produces zero-wait schedules in some replications. Deterministic dispatching rules remain competitive or better for expected benchmark throughput, although the particle swarm method finds the shortest best-case schedule for 30 surgeries. A retrospective replay of 120 historical elective operating days reproduces the throughput–waiting trade-off. Relative to the realized manual calendar span, the particle swarm method yields a median simulated makespan difference of 15.2% (median absolute difference: 86.3 min per day). The replay evaluates scheduling with realized procedure durations, daily room counts, and the fixed configured staff pool, providing a retrospective comparison across historical workloads.