  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.
