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 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.
Highlights
Advances in sensing, robotics, and wireless sensor networks have enabled the use of small unmanned aerial vehicles (UAVs) in environmental sensing applications such as crop monitoring [1,2,3], forest fire monitoring [4], and ecosystem management [5,6], as well as civil security applications such as border surveillance [7,8] and disaster management [9]
A variety of studies have indicated that small UAVs have a huge potential for package delivery, last-mile delivery, and emergency response [11], and studies have indicated that the use of small UAVs in emergency response applications would lead to significant cost reductions [12,13] because of their inexpensive operation, maintenance, and labor costs
The following are the main contributions of this article: (i) the FCMURP with uncertainty in the fuel consumption of all vehicles is modeled as a two-stage stochastic program with random recourse; (ii) sample average approximation (SAA) is used to obtain statistical estimates of the lower and upper bounds for the optimal solution of the two-stage stochastic program; (iii) a tabu search heuristic for finding suboptimal solutions for the two-stage stochastic model is proposed; and ; (iv) the performance achieved by SAA and the tabu search heuristic is corroborated through extensive computational experiments
Summary
Robotics, and wireless sensor networks have enabled the use of small unmanned aerial vehicles (UAVs) in environmental sensing applications such as crop monitoring [1,2,3], forest fire monitoring [4], and ecosystem management [5,6], as well as civil security applications such as border surveillance [7,8] and disaster management [9]. In the emergency response context, they have the potential to save lives by transporting and delivering much-needed food and water supplies over any terrain Worldwide, corporate companies such as Amazon and Google have begun field-testing their UAV capabilities through initiatives such as Amazon Prime Air [13] and Project. The fuel a vehicle consumes to travel between any pair of targets/depots is uncertain, and the description of this uncertainty is known via numerous samples. In this context, the following two-stage Fuel-Constrained.
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