Post-Disaster Repair Crew Assignment Optimization Using Minimum Latency

Across infrastructure domains, physical damage caused by storms and other weather events often requires costly and time-sensitive repairs to restore services as quickly as possible. While previous studies have used agent-based models to estimate the cost of the repairs, the implemented strategies for assignment of repair crews to different locations are generally human-driven or based on simple rules. In order to find performant strategies, we model this problem as a combinational optimization problem known as the Minimum Weighted Latency Problem (MWLP) for $m$ repair crews. We apply a partitioning algorithm that balances assigning targets amongst all the crews using two different heuristics that optimize either the importance of repair locations or the travel time between them. We benchmark our algorithm on both randomly generated graphs as well as data derived from a real-world urban environment, and show that our algorithm delivers significantly better assignments than existing methods.