首页|混合时间窗下携带医疗物资的家庭医护人员调度问题

混合时间窗下携带医疗物资的家庭医护人员调度问题

扫码查看
原文链接
  • 万方数据
  • 维普
以家庭医护人员调度问题为研究对象,在模型中考虑混合时间窗与携带医疗物资等约束,设计分支定价算法求解,在算法中对分支过程与标签算法进行改进.在数值实验部分将本文提出的分支定价算法与自适应大邻域搜索算法和CPLEX作性能比较,80%以上的算例运用分支定价算法能求得最优解,而运用CPLEX算法仅有不到20%的算例能求得最优解,大规模的算例中分支定价算法相对于自适应大邻域算法的改进效果可达 15%以上.最后对模型中的关键参数进行灵敏度分析.实验结果表明,不同参数的医患匹配与违反时间窗的惩罚都将影响运营成本;考虑混合时间窗要优于软时间窗和硬时间窗,混合时间窗下的总成本相较于硬时间窗节省了约 2%,且避免了极端情形的出现;携带医疗物资的约束对总成本的影响最高可达 40%,因此在模型中考虑携带医疗物资是必要的.
Scheduling of Home Healthcare Workers Carrying Medical Supplies with Mixed Time Windows
The study focuses on the scheduling issue of home healthcare workers,incorporating constraints related to mixed time windows and medical supplies into the model.A branch-and-price algorithm is designed for solving the problem,with the branch process and labeling algorithm are improved within the algorithm.In the numerical experiments,the proposed branch-and-price algorithm is compared with the Adaptive Large Neighborhood Search(ALNS)algorithm and CPLEX in terms of performance.The branch-and-price algorithm finds optimal solutions in over 80%of the cases,whereas CPLEX achieves this in less than 20%of the cases.Moreover,in large-scale instances,the branch-and-price algorithm show an improvement of over 15%compared with ALNS algorithm.Sensitivity analysis is conducted on key parameters in the model.Experimental results indicate that different parameters for patient-caregiver matching and penalties for time window violations both impact operational costs;mixed time windows are more efficient than soft and hard time windows,with the total cost under mixed time windows being about 2%lower than that under hard time windows,while also avoiding extreme scenarios;the constraint of carrying medical supplies can affect the total cost by up to 40%,making it a necessary consideration for this constraint in the model.

home healthcaresynchronized servicemixed time windowmedical suppliesbranch and price

李妍峰、王海瑞

展开 >

西南交通大学 经济管理学院,四川 成都 610031

服务科学与创新四川省重点实验室,四川 成都 610031

家庭医护 同步服务 混合时间窗 医疗物资 分支定价

国家自然科学基金资助项目国家自然科学基金资助项目四川省自然科学基金资助项目四川省自然科学基金资助项目四川省科技厅应用基础研究资助项目西南交通大学智慧物流与供应链管理研究生导师团队资助项目

72071161718011812022NSFSC04672022NSFSC04772020YJ0220YJSY-DSTD201918

2024

10.3969/j.issn.1007-7375.240038

工业工程
广东工业大学

工业工程

CSTPCDCHSSCD
影响因子：0.691
ISSN：1007-7375
年,卷(期)：2024.27(5)