Genetic Algorithm-based Routing and Scheduling for Wildfire Suppression using a Team of UAVs

TL;DR

This paper introduces GARST, a genetic algorithm-based method for routing and scheduling UAVs to detect and extinguish wildfires efficiently, ensuring timely response and resource optimization.

Contribution

It presents a novel genetic algorithm approach for wildfire UAV routing and scheduling with time constraints, addressing NP-complete challenges and infeasible scenarios.

Findings

GARST effectively computes routes to reach fire sites before escalation.

The algorithm handles infeasible scenarios and maintains solution diversity.

It optimizes total fire quench time with high efficiency.

Abstract

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.