Due to the ageing population and the prevalence of chronic diseases, Home Health Care (HHC) practices are significantly increasing in developed countries to provide coordinated health related services to patients at their homes. Accordingly, the scope of HHC services is also expanding from typical nursing and postoperative care at home to cover all types of needs of elderly patients (e.g., personal care, drug delivery and meal services). This paper aims to address the pressing demand for HHC services and develop a novel and effective mathematical model and solution methodology for supporting health care service delivery decisions. Our decision support framework captures the real needs of HHC services, including the challenges of creating simultaneous schedules and route plans for a set of HHC staff and Home Delivery Vehicles (HDVs) under the requirements of synchronization between HHC staff and HDVs visits, multiple visits to patients, multiple routes of HDVs and pickup/delivery visits related precedence for HDVs. A Mixed Integer Linear Programming (MILP) model is developed to characterize the optimization problem. Considering the computational complexity of the problem, a Hybrid Genetic Algorithm (HGA) is proposed to suggest HHC planning decisions. The model formulation and proposed HGA are examined on real-life instances for demonstrating its practicality and randomly generated test instances for assessing the scalability of the proposed approach. The results show the effectiveness and efficiency of our solution methodology. Experimental results indicate that the proposed algorithm provided a good performance even with an increasing number of required synchronized services, whereas the heuristic tactics facilitate the HGA to produce better-quality solutions in a significantly shorter time. Our framework is expected to contribute to an important aspect of shared healthcare mobility.

由于人口老龄化和慢性病的流行，发达国家的家庭保健（HHC）做法正在显着增加，以向患者在家中提供协调的健康相关服务。因此，HHC服务的范围也从在家中的典型护理和术后护理扩展到涵盖老年患者的所有类型的需求（例如，个人护理，药物递送和餐饮服务）。本文旨在解决对HHC服务的紧迫需求，并开发一种新颖且有效的数学模型和解决方案方法来支持卫生保健服务提供决策。我们的决策支持框架涵盖了HHC服务的实际需求，包括在HHC工作人员与HDV访问之间，患者多次访问，HDV的多条路线以及接/送相关的同步要求下，为一组HHC人员和送货车（HDV）同时制定时间表和路线计划的挑战HDV的优先级。开发了混合整数线性规划（MILP）模型来描述优化问题。考虑到问题的计算复杂性，提出了一种混合遗传算法（HGA）来建议HHC规划决策。在真实的实例上检查了模型公式和建议的HGA，以证明其实用性，并在随机生成的测试实例上评估了所建议方法的可伸缩性。结果显示了我们解决方案方法的有效性和效率。实验结果表明，提出的算法即使在所需的同步服务数量增加的情况下也能提供良好的性能，而启发式策略有助于HGA在明显较短的时间内产生质量更高的解决方案。我们的框架有望为共享医疗保健流动性的重要方面做出贡献。