Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS.

翻译：疏散规划是灾害管理的关键环节。然而，同时优化其中两个核心组成部分——路径规划与调度方案，并以最小化平均疏散时间或疏散完成时间等为目标，属于计算复杂问题。针对该问题，我们提出MIP-LNS这一可扩展优化方法，该方法采用启发式搜索与数学优化相结合的策略，能够优化多种目标函数。同时，我们提出MIP-LNS-SIM方法，将基于智能体的仿真与MIP-LNS相结合，用于估计拥塞导致的延迟，并在此类延迟约束下寻找优化方案。以德克萨斯州休斯顿市哈里斯县为研究区域，结果表明：在给定时间限制内，MIP-LNS在三种不同评估指标下均优于现有方法；然而，在考虑拥塞依赖延迟时，MIP-LNS-SIM在多项性能指标上优于MIP-LNS。此外，与MIP-LNS相比，MIP-LNS-SIM对疏散完成时间的估计百分比误差显著更低。