论文标题
自适应灾害物流计划的多阶段随机编程方法
Multi-stage Stochastic Programming Methods for Adaptive Disaster Relief Logistics Planning
论文作者
论文摘要
我们考虑一个物流计划问题，即准备救济物品，以准备即将发生的飓风登陆。此问题被建模为多重编码网络流问题，其中的目的是最大程度地减少操作网络的物流成本，并对需求不满意的需求罚款。我们假设对救济物品的需求可以从飓风的预测强度和登陆位置得出，该地点根据马尔可夫链的发展而发展。我们在两个不同的环境中考虑了这个问题，具体取决于登陆的时间是确定性的（并已知先验）还是随机的。对于前一种情况，我们引入了一个完全自适应的多阶段随机编程（MSP）模型，该模型允许决策者在多个阶段中依次调整介词决策，因为飓风的特征变得更加清晰。对于后一种情况，我们将MSP模型扩展为guigues（2021）中引入的随机数量的阶段，并将基础随机过程假定为阶段依赖性。我们使用其他近似策略（例如静态和滚动）两阶段随机编程方法等其他近似策略对MSP模型的性能进行基准测试。我们的数值结果为救灾物流规划中的MSP价值提供了关键的见解。
We consider a logistics planning problem of prepositioning relief items in preparation for an impending hurricane landfall. This problem is modeled as a multiperiod network flow problem where the objective is to minimize the logistics cost of operating the network and the penalty for unsatisfied demand. We assume that the demand for relief items can be derived from the hurricane's predicted intensity and landfall location, which evolves according to a Markov chain. We consider this problem in two different settings, depending on whether the time of landfall is assumed to be deterministic (and known a priori) or random. For the former case, we introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adjust the prepositioning decisions, sequentially, over multiple stages, as the hurricane's characteristics become clearer. For the latter case, we extend the MSP model with a random number of stages introduced in Guigues (2021), to the case where the underlying stochastic process is assumed to be stage-wise dependent. We benchmark the performance of the MSP models with other approximation policies such as the static and rolling-horizon two-stage stochastic programming approaches. Our numerical results provide key insight into the value of MSP, in disaster relief logistics planning.
