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

TL;DR

The paper tackles early wildfire suppression by routing and scheduling a homogeneous team of UAVs to mitigate fires as Single UAV Tasks (SUT) before they escalate to Multi-UAV Tasks (MUT), a problem shown to be NP-complete. It introduces GARST, a genetic-algorithm framework that encodes routes as two-part chromosomes and minimizes the total quench time, $J = \sum_{i=1}^{m} (\sum_{j=1}^{n} T^q_{ij})$, while enforcing that each fire is started before its deadline $T^d_j$ and that critical area constraints $A^c = \left(\frac{\\phi^q}{2\\sqrt{\\pi} \\phi^s}\right)^2$ govern feasibility. GARST integrates fitness-based initialization, selection, single-point crossover with repair, and mutation guided by an infeasibility ratio, plus elitism to preserve top performers, addressing the problem’s NP-complete nature and infeasibility scenarios. Empirical results in a 1 km by 1 km area with five UAVs show GARST achieving 100% success for up to four times the number of fires relative to UAVs and 93% for five times, significantly reducing average quench times compared to naive routing. The work demonstrates practical potential for scalable, early-stage wildfire management and motivates future decentralized extensions and improved genetic operators to enhance convergence and robustness.

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.

Figures (3)

• Figure 1: A wildfire management scenario showing the routing and scheduling for a team of UAVs
• Figure 2: Representation of an encoded chromosome
• Figure 3: Routing and scheduling solution for a scenario with 5 UAVs (a) 15 fires (b) 20 fires, and (c) 25 fires.