论文标题
对移动医疗部门的强大战略规划,可通道和不可移动的需求
Robust strategic planning for mobile medical units with steerable and unsteerable demands
论文作者
论文摘要
移动医疗单元(MMU)是配备有医疗设备的定制车辆,用于在农村环境中提供初级保健。由于可以轻松地重新安置MMU,因此可以实现以需求为导向,灵活且本地提供卫生服务的服务。在本文中,我们通过确定应该在哪里设置MMU操作站点以及应为其提供多久提供的MMU服务的战略规划。为此,我们研究了MMU(SPMMU)$ - $的战略计划问题 - $ $一个电容套装覆盖问题,包括现有实践和两种类型的患者需求:(i)代表通过集中任命系统寻求健康服务的患者,可以在给定的考虑设置和(ii)不知情的患者中始终拜访的任何治疗设施。我们提出了一个用于SPMMU的整数线性程序,可以通过弯曲器分解和约束生成来解决。从这种公式开始,我们关注的是不确定的问题的问题,在该问题中,将可检测和不可行动的需求建模为随机变量,这些变量可能会在给定的间隔内变化。使用来自强大的优化和二元性理论的方法,我们设计了确切的约束生成方法,以解决可靠的对应物,以解决间隔和预算的不确定性集。我们所有的结果转移到了特定于会议的SPMMU,我们在一项基于德国农村初级保健系统产生的实例中评估了我们的模型。
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.