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This study aims to evaluate three AI-based optimization algorithms—Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Ant Colony Optimization (ACO)—for their effectiveness in optimizing AMR scheduling in manufacturing environments. Through simulations in MATLAB, it was found that ACO demonstrated consistent performance with minimal processing times, making it suitable for real-time applications. Conversely, while GA and PSO reduced overall travel distance, they exhibited nonlinear growth in processing times, indicating potential computational challenges. The findings suggest that ACO may offer a more balanced approach for real-time Autonomous Mobile Robot(AMRs) scheduling optimization, providing a base for further exploration.
Wu et al. (Mon,) studied this question.