Post-Disaster Repair Crew Assignment Optimization Using Minimum Latency

TL;DR

This paper presents an optimization approach for assigning repair crews after disasters, using a partitioning algorithm to minimize latency and improve repair efficiency in urban environments.

Contribution

It introduces a novel application of the Minimum Weighted Latency Problem with heuristics for repair crew assignment in disaster scenarios.

Findings

Algorithm outperforms existing methods in benchmark tests.

Heuristics effectively balance repair importance and travel time.

Significant reduction in repair latency achieved.

Abstract

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 recent studies have used agent-based models to estimate the cost of 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 continue with an agent-based model, but approach this problem as a combinational optimization problem known as the Minimum Weighted Latency Problem for multiple repair crews. We apply a partitioning algorithm that balances the assignment of 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.