AMACO for Adaptive and Efficient Task Allocation in Medical Environments

Abstract

The increasing complexity of medical tasks has imposed significant challenges on existing task allocation systems in healthcare. This study introduces a flexible multi-objective optimization model utilizing goal-directed optimization to address this challenge. The model prioritizes urgent medical tasks, aiming to minimize the potential loss of task value and reduce patient health risks. Simultaneously, it strives to optimize the use of available resources to ensure the sustainability of operations. To address the optimization problem, an Adaptive Multi-Objective Ant Colony Optimization algorithm was introduced. This approach incorporates a dynamic heuristic function and a flexible pheromone initialization mechanism to enhance the efficiency and accuracy of task allocation. The experimental results demonstrate that the proposed algorithm outperforms others in terms of convergence speed, solution quality, and flexibility, providing an effective tool for reducing the burden on medical staff and improving the operational efficiency of healthcare institutions.