A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

TL;DR

This paper introduces a two-stage stochastic optimization approach for routing multiple UAVs with uncertain fuel consumption, improving efficiency and solution quality over deterministic models.

Contribution

It formulates a novel two-stage stochastic model for UAV routing with refueling constraints and develops a tabu-search heuristic for large-scale problems.

Findings

Two-stage model outperforms deterministic approaches in fuel efficiency.

The heuristic provides high-quality solutions with reduced computation time.

Computational experiments validate the model's effectiveness and heuristic's efficiency.

Abstract

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.