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Across infrastructure domains, physical damage caused by storms and otherweather events often requires costly and time-sensitive repairs to restoreservices as quickly as possible. While recent studies have used agent-basedmodels to estimate the cost of repairs, the implemented strategies forassignment of repair crews to different locations are generally human-driven orbased on simple rules. In order to find performant strategies, we continue withan agent-based model, but approach this problem as a combinational optimizationproblem known as the Minimum Weighted Latency Problem for multiple repaircrews. We apply a partitioning algorithm that balances the assignment oftargets amongst all the crews using two different heuristics that optimizeeither the importance of repair locations or the travel time between them. Webenchmark our algorithm on both randomly generated graphs as well as dataderived from a real-world urban environment, and show that our algorithmdelivers significantly better assignments than existing methods.

Post-Disaster Repair Crew Assignment Optimization Using Minimum Latency