Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals

Marcelo Becerra-Rozas; Bady Gana; José Lara; Andres Leiva-Araos; Broderick Crawford; José M. Gómez Pulido; Cristian Contreras; José J. Caro-Miranda; Miguel García-Remesal

doi:10.3390/biomimetics11030183

Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals

Journal

Biomimetics

ISSN

2313-7673

Date Issued

2026-03-03

Author(s)

Marcelo Becerra-Rozas

Bady Gana

José Lara

Andres Leiva-Araos

Facultad de Ingeniería

Broderick Crawford

José M. Gómez Pulido

Cristian Contreras

José J. Caro-Miranda

Miguel García-Remesal

Type

journal-article

DOI

10.3390/biomimetics11030183

URL

https://hdl.handle.net/123456789/12274

Abstract

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are assessed within a dynamic three-stage flexible flow shop model under no-buffer blocking constraints. Findings from 300 Monte Carlo replications demonstrate that while the Genetic Algorithm achieves peak global efficiency, discretized bio-inspired algorithms reach a comparable statistical efficiency frontier. Notably, the discretized Secretary Bird Optimization Algorithm facilitates superior emergency integration by maintaining natural capacity buffers, whereas the aggressive local optimization characteristic of alternative methods often triggers resource saturation in recovery units. These results indicate a potential recovery of 90 annual operating hours per theater.These results indicate a potential recovery of 90 annual operating hours per theater, representing a 6.7% increase in resource utilization efficiency. This improvement provides a critical data-driven capacity margin to mitigate the non-prioritized (Non-GES) surgical backlog in Chilean public hospitals.