Optimization Approaches for the Traveling Salesman Problem with Drone

The efficiency and dynamism of unmanned aerial vehicles, or drones, have presented substantial application opportunities in several industries in the last years. Notably, logistic companies have given close attention to these vehicles to reduce delivery time and operational cost. A variant of the traveling salesman problem (TSP), called the flying sidekick traveling salesman problem, was introduced involving drone‐assisted parcel delivery. The drone launches from the truck, proceeds to deliver parcels to a customer, and then is recovered by the truck at a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic where the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the general variable neighborhood search is employed to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve significantly delivery time. Furthermore, we provide a new set of instances based on the well‐known traveling salesman problem library instances.

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“…Table , taken from Agatz et al. (), summarizes truck and drone characteristics. The complementary features of each vehicle are indicated in bold.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed heuristic addresses the traveling salesman problem (TSP) with drones proposed by Murray and Chu () and Agatz et al. (). We propose a variable neighborhood search (VNS) based heuristic called hybrid general variable neighborhood search (HGVNS) to tackle the TSP with drones problem.…”

Section: Introductionmentioning

confidence: 99%

A variable neighborhood search for flying sidekick traveling salesman problem

Freitas

Penna

2019

Int Trans Operational Res

152

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“…Unmanned aerial vehicles have a wide application in parcel delivery, ground target monitoring, smart city management, etc. Envisioning the flourish of UAVs in the future, the academy and industry communities have proposed multiple technologies to manage the UAV network.…”

Section: Related Workmentioning

confidence: 99%

When UAVs coexist with manned airplanes: Large‐scale aerial network management using ADS‐B

Pan

et al. 2019

Trans Emerging Tel Tech

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Increasing attention has been paid to employing the Automatic Dependent Surveillance‐Broadcast (ADS‐B) technology on unmanned area vehicles (UAVs). The critical challenge therefore emerges, ie, how to effectively schedule the UAVs without interfering the communication. An UAV management framework named uFly is proposed to address this challenge by controlling the total ADS‐B communication traffic over the air. The ADS‐B's interference probability is first modeled. Based on this model, the UAVs' ADS‐B traffic (as well as the number of flying UAVs) should match the manned airplanes' signals. By taking the manned airplanes' signals as the first‐class constraint, uFly then dynamically approves or denies UAVs for flight based on the real‐time number of airplanes. Finally, experiments using different data sets are conducted to verify the superior performance of uFly.

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“…One broad categorization of related problems involves surveillance applications with cooperative air and ground assets [5,23]. There are also certain kinds of cooperative routing problems for GV and UAV, called TSP with drone (TSP-D) [24,25,26,27] and VRP with drone (VRP-D) [28,29]. In these problems, the UAV is carried by a mobile platform that keeps on moving on its own route while UAV executes its mission.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Echelon Cooperated Routing Problem for a Ground Vehicle and Its Carried Unmanned Aerial Vehicle

Luo

Liu

Shi

2017

Sensors

129

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In this paper, a two-echelon cooperated routing problem for the ground vehicle (GV) and its carried unmanned aerial vehicle (UAV) is investigated, where the GV travels on the road network and its UAV travels in areas beyond the road to visit a number of targets unreached by the GV. In contrast to the classical two-echelon routing problem, the UAV has to launch and land on the GV frequently to change or charge its battery while the GV is moving on the road network. A new 0–1 integer programming model is developed to formulate the problem, where the constraints on the spatial and temporal cooperation of GV and UAV routes are included. Two heuristics are proposed to solve the model: the first heuristic (H1) constructs a complete tour for all targets and splits it by GV routes, while the second heuristic (H2) constructs the GV tour and assigns UAV flights to it. Random instances with six different sizes (25–200 targets, 12–80 rendezvous nodes) are used to test the algorithms. Computational results show that H1 performs slightly better than H2, while H2 uses less time and is more stable.

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Optimization Approaches for the Traveling Salesman Problem with Drone

Cited by 47 publications

References 21 publications

A variable neighborhood search for flying sidekick traveling salesman problem

A variable neighborhood search for flying sidekick traveling salesman problem

When UAVs coexist with manned airplanes: Large‐scale aerial network management using ADS‐B

A Two-Echelon Cooperated Routing Problem for a Ground Vehicle and Its Carried Unmanned Aerial Vehicle

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