Estimating the Tour Length for the Close Enough Traveling Salesman Problem

Roy, Debdatta Sinha; Golden, Bruce L.; Wang, Xingyin; Wasil, Edward

doi:10.3390/a14040123

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Ref. [40] proposed a branch-andbound algorithm to address the CETSP in two and three dimensions, and [17] presented a method to estimate previously a tour length from the knowledge of several variables. Refs.…”

Section: Related Workmentioning

confidence: 99%

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Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

et al. 2023

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Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function as the minimal distance, time or energy. However, in some applications, as the one addressed in this paper—namely the data collection of buried sensor nodes by means of an Unmanned Aerial Vehicle— the targets are areas with varying sizes: they are defined with respect to the radio communication range of each node, ranging from a few meters to several hundred meters according to various parameters (e.g., soil moisture, burial depth, transmit power). The Unmanned Aerial Vehicle has to enter successively in these dynamic areas to collect the data, without the need to pass at the vertical of each node. Some areas can obviously intersect. That leads to solve the Close Enough TSP. To determine a sub-optimal trajectory for the Unmanned Aerial Vehicle, this paper presents an original and efficient strategy based on an evolutionary algorithm completed with geometrical heuristics. The performances of the algorithm are highlighted through scenarios with respectively 15 and 50 target locations. The results are analyzed with respect to the total route length. Finally, conclusions and future research directions are discussed.

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Section: Related Workmentioning

confidence: 99%

“…The problem is called TSP with Neighborhoods (TSPN). A similar problem is the Close Enough TSP (CETSP) where the areas are defined by circles of different radius-see [17,18].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

et al. 2023

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“…Yet, a recent review paper found that there have been extraordinarily few studies of the potential to transport dangerous goods by aircraft [50]. The literature mainly covered risk assessment [51], traffic flow prediction [52], vehicle routing [53], tour length optimization [54], facility locating, scheduling, aircraft loading [55], emergency response [56], and accident cause analysis [40].…”

Section: Transporting Dangerous Goodsmentioning

confidence: 99%

Reducing Risks by Transporting Dangerous Cargo in Drones

Bridgelall

2022

Sustainability

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The transportation of dangerous goods by truck or railway multiplies the risk of harm to people and the environment when accidents occur. Many manufacturers are developing autonomous drones that can fly heavy cargo and safely integrate into the national air space. Those developments present an opportunity to not only diminish risk but also to decrease cost and ground traffic congestion by moving certain types of dangerous cargo by air. This work identified a minimal set of metropolitan areas where initial cargo drone deployments would be the most impactful in demonstrating the safety, efficiency, and environmental benefits of this technology. The contribution is a new hybrid data mining workflow that combines unsupervised machine learning (UML) and geospatial information system (GIS) techniques to inform managerial or investment decision making. The data mining and UML techniques transformed comprehensive origin–destination records of more than 40 commodity category movements to identify a minimal set of metropolitan statistical areas (MSAs) with the greatest demand for transporting dangerous goods. The GIS part of the workflow determined the geodesic distances between and within all pairwise combinations of MSAs in the continental United States. The case study of applying the workflow to a commodity category of dangerous goods revealed that cargo drone deployments in only nine MSAs in four U.S. states can transport 38% of those commodities within 400 miles. The analysis concludes that future cargo drone technology has the potential to replace the equivalent of 4.7 million North American semitrailer trucks that currently move dangerous cargo through populated communities.

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