面向拥塞相关延迟的大规模疏散规划仿真辅助优化
Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays
AI总结：
针对大规模疏散规划中路径与调度联合优化的计算难题，提出结合启发式搜索与数学优化的MIP-LNS方法，以及融合智能体仿真的MIP-LNS-SIM方法，后者在考虑拥塞延迟时性能更优、时间估算误差更低。
摘要：
疏散规划是灾害管理的关键环节。然而，对其两个核心组成部分——路径规划与调度——进行联合优化，以实现平均疏散时间或疏散完成时间最小化等目标，是一个计算难度极高的问题。为解决该问题，我们提出MIP-LNS，这是一种可扩展的优化方法，结合启发式搜索与数学优化，可对多种目标函数进行优化。我们还提出MIP-LNS-SIM方法，将基于智能体的仿真与MIP-LNS相结合，用于估算拥塞导致的延迟，并在考虑此类延迟的情况下找到优化方案。我们以德克萨斯州休斯顿市的哈里斯县作为研究区域。实验表明，在给定时间限制内，MIP-LNS在三个不同指标上的求解效果优于现有方法。但当考虑拥塞相关延迟时，MIP-LNS-SIM在多项性能指标上均优于MIP-LNS。此外，与MIP-LNS相比，MIP-LNS-SIM在疏散完成时间估算上的百分比误差显著更低。
英文摘要：
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.
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