具有可操纵和不可操纵需求的机动医疗分队的稳健战略规划
Robust strategic planning for mobile medical units with steerable and unsteerable demands
摘要：
流动医疗队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。由于医疗保健单位可以很容易地重新安置，它们能够以需求为导向，灵活地提供当地卫生服务。在本文中，我们调查的MMU服务的战略规划，决定MMU操作网站应该建立在哪里，以及多久这些应该得到服务。为此，我们研究了MMU（SPMMU）的战略规划问题$-$，这是一个包含现有实践和两种类型患者需求的能力集覆盖问题：（i）可转向需求，代表通过集中预约系统寻求医疗服务的患者，并且可以在给定的考虑集内被引导到任何治疗设施;以及（ii）不可转向需求，代表步行-总是去最近的治疗机构就诊的患者。我们提出了一个整数线性规划的SPMMU，可以通过Benders分解和约束生成来解决。从这个配方开始，我们专注于不确定版本的问题，其中可操纵和不可操纵的需求建模为随机变量，可能会在一个给定的时间间隔内变化。利用鲁棒优化和对偶理论的方法，我们设计了精确的约束生成方法来解决区间和预算不确定性集的鲁棒对应。我们所有的结果转移到特定的会话SPMMU，我们评估我们的模型在计算研究的基础上，从德国农村初级保健系统产生的一组实例。
英文摘要：
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 (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 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
