The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). To fulfill a mission, the NRF operates a number of technical systems, mostly vehicles. Each system is composed of several parts which might fail over time, and it can only be used again in the mission if all broken parts are replaced. For short deployments (e.g., one month), the NRF troops bring with them a tightly constrained “warehouse” of spare parts. To ensure an optimal use of the space, we present a two-stage stochastic programming model where in the first stage spare parts are chosen, then failures occur at random, and in the second stage the parts are assigned to the broken systems. We carry out a scenario-based approach, where the failures are simulated by a Monte-Carlo approach. We demonstrate that the resulting mixed-integer linear program can be solved using standard numerical solvers. Using real-world input data provided by the German Armed Forces and simulated data, we analyze the sensitivity of the solutions with respect to the size of the warehouse, the service level, and the number of scenarios, and compare our approach with simpler, doctrine based warehousing strategies.

德国武装部队提供了一支行动特遣队，以支持北大西洋公约组织 (NATO) 反应部队 (NRF)。为了完成任务，NRF 运行了许多技术系统，主要是车辆。每个系统由几个部分组成，随着时间的推移可能会出现故障，并且只有在更换所有损坏的部件后才能在任务中再次使用。对于短期部署（例如，一个月），NRF 部队会带来一个严格限制的备件“仓库”。为了确保空间的最佳利用，我们提出了一个两阶段随机规划模型，其中在第一阶段选择备件，然后随机发生故障，在第二阶段将部件分配给损坏的系统。我们采用基于场景的方法，其中故障通过蒙特卡洛方法进行模拟。我们证明可以使用标准数值求解器来求解得到的混合整数线性程序。使用德国武装部队提供的真实世界输入数据和模拟数据，我们分析了解决方案在仓库大小、服务水平和场景数量方面的敏感性，并将我们的方法与更简单的理论进行比较基于仓储策略。