A Parallel Genetic Algorithm With Variable Neighborhood Search for the Vehicle Routing Problem in Forest Fire-Fighting

Abstract

With the challenge of making online decisions in the face of escalating losses, the vehicle routing problem in forest fire-fighting places great emphasis on the computing efficiency of algorithms. This paper investigates the route optimization for fire-fighting vehicles and formulates a mixed integer linear programming model to minimize the total losses of overall fire spots. A parallel genetic algorithm with variable neighborhood search (PGA-VNS) is developed, which includes a parallelized evolution process of multiple sub-populations for ensuring the fast convergency to a high-quality solution, and a multi-operator variable searching process to promote the convergency to a nearly optimal solution. By comparing the computational results for various-scale instances, the PGA-VNS is demonstrated to be more effective than the baseline algorithms (i.e., Gurobi, GA, PSO, ALNS, VNS, GA-ALNS, and GA-VNS). Furthermore, the proposed model is extended to the scenario of unmanned aerial vehicles (UAVs), taking into account limited flight range and load impact constraints. The results could support the scheduling and routing for fire engines or UAVs in minimizing the losses caused by forest fires.