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.

1. [1] S. Palani and K. Rameshbabu, “A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications,” Measurement: Sensors, Vol.31, Article No.100988, 2024. https://doi.org/10.1016/j.measen.2023.100988
2. [2] M. Zhao et al., “Energy-aware task offloading and resource allocation for time-sensitive services in mobile edge computing systems,” IEEE Trans. on Vehicular Technology, Vol.70, No.10, pp. 10925-10940, 2021. https://doi.org/10.1109/TVT.2021.3108508
3. [3] J. Jiang et al., “Data-driven collaborative healthcare resource allocation in pandemics,” Transportation Research Part E: Logistics and Transportation Review, Vol.192, Article No.103828, 2024. https://doi.org/10.1016/j.tre.2024.103828
4. [4] A. Ala, A. Goli, S. Mirjalili, and V. Simic, “A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design,” Applied Soft Computing, Vol.150, Article No.111012, 2024. https://doi.org/10.1016/j.asoc.2023.111012
5. [5] L. Yao et al., “Investigation of the knowledge, attitude and behavior of medical personnel and related needs for emergency rescue in public health emergencies,” Int. Emergency Nursing, Vol.77, Article No.101531, 2024. https://doi.org/10.1016/j.ienj.2024.101531
6. [6] K. Lin, S. Pankaj, and D. Wang, “Task offloading and resource allocation for edge-of-things computing on smart healthcare systems,” Computers & Electrical Engineering, Vol.72, pp. 348-360, 2018. https://doi.org/10.1016/j.compeleceng.2018.10.003
7. [7] S. O’Meara, “Medical robotics in China: The rise of technology in three charts,” Nature, Vol.582, No.S51, pp. S51-S52, 2020. https://doi.org/10.1038/d41586-020-01795-7
8. [8] H. Chakraa, F. Guérin, E. Leclercq, and D. Lefebvre, “Optimization techniques for multi-robot task allocation problems: Review on the state-of-the-art,” Robotics and Autonomous Systems, Vol.168, Article No.104492, 2023. https://doi.org/10.1016/j.robot.2023.104492
9. [9] J. Wu and Y. Liu, “A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem,” Engineering Applications of Artificial Intelligence, Vol.140, Article No.109688, 2025. https://doi.org/10.1016/j.engappai.2024.109688
10. [10] Y. Hai, X. Xu, and Z. Liu, “Dynamic multi-objective service composition based on improved social learning optimization algorithm,” Applied Soft Computing, Vol.167, Part A, Article No.112266, 2024. https://doi.org/10.1016/j.asoc.2024.112266
11. [11] W. Zhang, S. Wang, G. Li, W. Zhang, and X. Wang, “A dual-sampling based evolutionary algorithm for large-scale multi-objective optimization,” Applied Soft Computing, Vol.167, Part B, Article No.112344, 2024. https://doi.org/10.1016/j.asoc.2024.112344
12. [12] C. Wei, Z. Ji, and B. Cai, “Particle swarm optimization for cooperative multi-robot task allocation: A multi-objective approach,” IEEE Robotics and Automation Letters, Vol.5, No.2, pp. 2530-2537, 2020. https://doi.org/10.1109/LRA.2020.2972894
13. [13] X. Meng and H. Li, “An adaptive co-evolutionary competitive particle swarm optimizer for constrained multi-objective optimization problems,” Swarm and Evolutionary Computation, Vol.91, Article No.101746, 2024. https://doi.org/10.1016/j.swevo.2024.101746
14. [14] A. Ernst, H. Jiang, and M. Krishnamoorthy, “Exact solutions to task allocation problems,” Management Science, Vol.52, No.10, pp. 1634-1646, 2006. https://doi.org/10.1287/mnsc.1060.0578
15. [15] A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” Proc. of the European Conf. on Artificial Life, pp. 134-142, 1991.
16. [16] S. Wu et al., “Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning,” Applied Soft Computing, Vol.167, Part C, Article No.112433, 2024. https://doi.org/10.1016/j.asoc.2024.112433
17. [17] C. Blum, “Ant colony optimization: A bibliometric review,” Physics of Life Reviews, Vol.51, pp. 87-95, 2024. https://doi.org/10.1016/j.plrev.2024.09.014
18. [18] T. Cai et al., “Multi-label feature selection based on improved ant colony optimization algorithm with dynamic redundancy and label dependence,” Computers, Materials and Continua, Vol.81, No.1, pp. 1157-1175, 2024. https://doi.org/10.32604/cmc.2024.055080
19. [19] G. Starzec, M. Starzec, L. Rutkowski, M. Kisiel-Dorohinicki, and A. Byrski, “Ant colony optimization using two-dimensional pheromone for single-objective transport problems,” J. of Computational Science, Vol.79, Article No.102308, 2024. https://doi.org/10.1016/j.jocs.2024.102308
20. [20] M. Lopez-Ibanez and T. Stutzle, “The automatic design of multiobjective ant colony optimization algorithms,” IEEE Trans. on Evolutionary Computation, Vol.16, No.6, pp. 861-875, 2012. https://doi.org/10.1109/TEVC.2011.2182651
21. [21] L. Chen, W.-L. Liu, and J. Zhong, “An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles,” J. of Computational Science, Vol.58, Article No.101545, 2022. https://doi.org/10.1016/j.jocs.2022.101545
22. [22] J. Cui et al., “Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning,” Knowledge-Based Systems, Vol.288, Article No.111459, 2024. https://doi.org/10.1016/j.knosys.2024.111459
23. [23] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence,” The University of Michigan Press, 1975.
24. [24] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. of Int. Conf. on Neural Networks, Vol.4, pp. 1942-1948, 1995. https://doi.org/10.1109/ICNN.1995.488968