Total distance travelled for each schedule.

Despite much literature on operations research applied to various healthcare problems, impactful implementation in public healthcare is limited, which often results in allocative inefficiency. This article uses a mobile clinic routing and scheduling problem in the Witzenberg region of South Africa as a case study to demonstrate the improvement of implementation success through cross-disciplinary collaboration, and also to propose a new three-stage approach for modelling a mobile clinic problem that incorporates continuity of care, fairness, and minimisation of distance travelled. Mobile clinics are used in many countries to improve access to healthcare for rural communities. Decision makers must assign farms or villages to mobile clinics, and determine their monthly visit schedules. To improve implementation success, we follow a collaborative three-phased mixed-methods approach with healthcare professionals to improve workload balance, fairness, and transportation cost. During phase 1, qualitative and quantitative data are gathered through qualitative research methods. In phase 2, fairly distributed optimal routes and schedules are designed using a three-stage model that incorporates a multi-vehicle routing problem to determine daily routes, a knapsack problem to establish a fair allocation of these daily routes between different clinics, and another variation on the vehicle routing problem to determine the monthly visit schedule that minimises the distance between the last farm visited on each consecutive day in the case of having to return to a farm the next day. Different input parameter estimations result in different routes and schedules. In phase 3, AHP is performed with main decision makers to determine their preferred solution. Final routes and schedules are designed based on model results, AHP results, and contextual input from decision makers. In our case study, an improved workload balance, a 23% reduction in total distance travelled, and buy-in to implement the changes, were obtained.尽管现有大量针对各类医疗问题的运筹学（Operations Research）应用研究，但公共医疗领域中具备实际影响力的落地实践仍较为匮乏，这往往会导致资源配置效率低下。本文以南非维岑伯格（Witzenberg）地区的流动医疗车（Mobile Clinic）路径规划与排班问题为案例，旨在通过跨学科协作提升落地实施成功率，并提出一种全新的三阶段建模方法，用于建模流动医疗车调度问题，该方法兼顾医疗服务连续性、公平性与行驶里程最小化目标。诸多国家均采用流动医疗车以提升农村社区的医疗服务可及性。决策者需要将农场或村落分配给对应的流动医疗车，并制定月度出诊排班计划。为提升落地实施成功率，本文采用与医疗专业人员协同开展的三阶段混合研究方法，以优化工作负载均衡、资源分配公平性与运输成本。第一阶段，通过定性研究方法采集定性与定量数据。第二阶段，采用三阶段模型设计公平分配的最优路径与排班方案：其一，通过多车辆路径问题（Multi-Vehicle Routing Problem）确定单日行驶路径；其二，通过背包问题（Knapsack Problem）实现不同流动医疗车之间单日路径的公平分配；其三，通过车辆路径问题（Vehicle Routing Problem）的衍生模型确定月度出诊排班，该模型可在需次日重返某村落的场景下，最小化相邻两日最后到访村落间的行驶距离。不同的输入参数估计值会生成不同的路径与排班方案。第三阶段，与核心决策者开展层次分析法（Analytic Hierarchy Process, AHP）调研，以确定其偏好的解决方案。最终的路径与排班方案将结合模型输出、层次分析法结果以及决策者提供的场景化输入信息进行设计。在本次案例研究中，我们实现了工作负载均衡性的优化、总行驶里程降低23%，同时获得了相关方对方案落地的认可与支持。