Mobile medical units (MMUs) are customized vehicles fitted with medical equipment that are used to provide primary care in rural environments. As MMUs can be easily relocated, they enable a demand-oriented, flexible, and local provision of health services. In this paper, we investigate the strategic planning of an MMU service by deciding where MMU operation sites should be set up and how often these should be serviced. To that end, we study the strategic planning problem for MMUs ($\mathrm{SPMMU}$) – a capacitated set covering problem that includes existing practices and two types of patient demands: (i) steerable demands representing patients who seek health services through a centralized appointment system and can be steered to any treatment facility within a given consideration set and (ii) unsteerable demands representing walk-in patients who always visit the closest available treatment facility. We propose an integer linear program for the $\text{SPMMU}$ that can be solved via Benders decomposition and constraint generation. Starting from this formulation, we focus on the uncertain version of the problem in which steerable and unsteerable demands are modeled as random variables that may vary within a given interval. Using methods from robust optimization and duality theory, we devise exact constraint generation methods to solve the robust counterparts for interval and budgeted uncertainty sets. All our results transfer to the session-specific $\text{SPMMU}$ and we evaluate our models in a computational study based on a set of instances generated from a rural primary care system in Germany.

移动医疗单位 (MMU) 是配备医疗设备的定制车辆，用于在农村环境中提供初级保健。由于 MMU 可以轻松搬迁，因此它们能够实现以需求为导向、灵活和本地化的医疗服务。在本文中，我们通过决定 MMU 运营站点的设置位置以及这些站点的维护频率来研究 MMU 服务的战略规划。为此，我们研究了 MMU 的战略规划问题（$\mathrm{SPMMU}$) – 一个涵盖问题的能力集，包括现有的实践和两种类型的患者需求：(i) 可控制的需求，代表通过集中预约系统寻求医疗服务的患者，并且可以被引导到给定考虑集中的任何治疗机构和 (ii) ) 代表总是访问最近的可用治疗设施的未经预约的患者的无法控制的需求。我们提出了一个整数线性规划$\text{SPMMU}$这可以通过 Benders 分解和约束生成来解决。从这个公式开始，我们关注问题的不确定版本，其中可操纵和不可操纵的需求被建模为可能在给定区间内变化的随机变量。使用稳健优化和对偶理论的方法，我们设计了精确约束生成方法来解决区间和预算不确定性集的稳健对应项。我们所有的结果都转移到特定于会话的$\text{SPMMU}$ 我们在一项基于德国农村初级保健系统生成的一组实例的计算研究中评估我们的模型。