Abstract: To address the scheduling problem of multi-department cooperative treatment of patients under the condition of limited medical resources in a hospital, this paper proposes a collaborative treatment scheduling algorithm based on intelligent optimization. The proposed algorithm regards the cooperative treatment scheduling of doctors, nurses and patients in different scenarios as a multi-role cooperative control problem. In order to optimize the role's access behavior, we propose a decision-making model to guide the role to make the optimal access behavior, and introduce an intelligent optimization algorithm to optimize the decision-making model. For the case scenarios of collaborative treatment of patients, doctor ward rounds, and patient physical examinations, we conduct experiment to compare four scheduling strategies, includingthe random, shortest distance, maximum free space, and decision-making models, and comparatively analyze the performance of the genetic algorithms, particle swarm optimization, simulated annealing, and differential evolution in optimizing the decision-making models. The experimental results demonstrates that the decision-making model based on the differential evolution algorithm performs the best, and the optimized decision-making model can find feasible solutions in the case scenarios and also obtain the optimal scheduling results.

Key words: cooperative treatment scheduling, genetic algorithm, particle swarm algorithm, differential evolution, simulated annealing