Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays

TL;DR

This paper introduces a scalable optimization framework combining heuristic search, mathematical optimization, and agent-based simulation to improve large-scale evacuation planning by accounting for congestion delays, outperforming existing methods.

Contribution

The paper presents MIP-LNS and MIP-LNS-SIM, novel methods that integrate simulation with optimization to better handle congestion effects in evacuation planning.

Findings

MIP-LNS outperforms existing methods in solution quality within time limits.

MIP-LNS-SIM achieves better performance when considering congestion delays.

MIP-LNS-SIM provides more accurate evacuation time estimates with lower error.

Abstract

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.