广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 21-33.doi: 10.12052/gdutxb.220167

• 计算机科学与技术 • 上一篇    下一篇

基于智能优化的协作治疗调度算法

胡晓敏1, 许万森1, 段宇晖1, 李敏1,2   

  1. 1. 广东工业大学 计算机学院，广东 广州 510006;
    2. 广东工业大学 信息工程学院，广东 广州 510006
  • 收稿日期:2022-11-08 出版日期:2023-09-25 发布日期:2023-09-26
  • 通信作者: 李敏 (1978-),女,讲师,主要研究方向为智能调度、深度学习,E-mail:lmjsj@gdut.edu.cn
  • 作者简介:胡晓敏 (1983-),女,副教授,博士,主要研究方向为人工智能、群体智能、智能调度
  • 基金资助:
    广东省重点领域研发计划资助项目(2021B0101200002)；国家自然科学基金资助项目(62272108)

Collaborative Treatment Scheduling Algorithm Based on Intelligent Optimization

Hu Xiao-min1, Xu Wan-sen1, Duan Yu-hui1, Li Min1,2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-11-08 Online:2023-09-25 Published:2023-09-26

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摘要： 为解决医院在有限医疗资源条件下多部门协作治疗病人的调度问题，提出了一种基于智能优化的协作治疗调度算法。该算法将医生、护士和病人在不同场景下的协作治疗调度问题视为多角色的协同控制问题，为了优化角色的访问行为，提出了用于指导角色作出最优访问行为的决策模型，并引入智能优化算法优化决策模型。针对协作治疗病人、医生探查病房、病人体检的案例场景，实验对比基于随机、最短距离、最大空闲空间、决策模型的4种调度策略，并对比分析使用遗传算法、粒子群算法、模拟退火和差分演化在优化决策模型的性能表现。实验结论证明，基于差分演化算法对决策模型优化的性能表现最优，且其优化得出的决策模型在案例场景中均能找到可行解且调度结果更优。

关键词: 协作治疗调度, 遗传算法, 粒子群算法, 差分演化算法, 模拟退火算法

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

中图分类号: 

  • P315.69
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