Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases with several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases.

为潜在的灾难场景做好准备，使政府机构和人道主义组织能够在灾难袭来时做出有效反应。在文献中，通常采用两阶段随机规划模型来在预期灾难发生之前制定备灾计划。这些模型可能很难解决，因为复杂性会随着不确定性和相互依赖的决策的多种来源而增加。在这项研究中，我们提出一种整数L形算法来解决位置和网络恢复的集成模型，这是一个两阶段随机规划模型，用于确定不确定情况下应急设施和恢复资源的数量和位置。我们的算法可容纳第二阶段的二进制决策变量，这些变量用于指示可用于救济分配的网络的未损坏和已修复的道路。我们的计算结果表明，对于大量的灾难场景，由于算法的求解时间仅随着场景数量的增加而线性增加，因此我们的算法优于CPLEX。