  This paper addresses early wildfire management using a team of UAVs for the
mitigation of fires. The early detection and mitigation systems help in
alleviating the destruction with reduced resource utilization. A Genetic
Algorithm-based Routing and Scheduling with Time constraints (GARST) is
proposed to find the shortest schedule route to mitigate the fires as Single
UAV Tasks (SUT). The objective of GARST is to compute the route and schedule of
the UAVs so that the UAVS reach the assigned fire locations before the fire
becomes a Multi UAV Task (MUT) and completely quench the fire using the
extinguisher. The fitness function used for the genetic algorithm is the total
quench time for mitigation of total fires. The selection, crossover, mutation
operators, and elitist strategies collectively ensure the exploration and
exploitation of the solution space, maintaining genetic diversity, preventing
premature convergence, and preserving high-performing individuals for the
effective optimization of solutions. The GARST effectively addresses the
challenges posed by the NP-complete problem of routing and scheduling for
growing tasks with time constraints. The GARST is able to handle infeasible
scenarios effectively, contributing to the overall optimization of the wildfire
management system.
