The multiple traveling salesmen problem with moving targets

Stieber, Anke; Fügenschuh, Armin; Epp, Maria; Knapp, Matthias; Rothe, Hendrik

doi:10.1007/s11590-014-0835-6

Cited by 18 publications

(14 citation statements)

References 5 publications

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“…TSP can be divided into a multitude of different problems [15,19]. However, the literature on TSP is still rare with regards to moving targets, like in fishing [3], surveillance [11] or in military applications [5,23]. These research topics are grouped under the title of "Dynamic Traveling Salesman Problem ", DTSP, with two main versions existing in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…However, the problem involving moving targets, or Dynamic TSP (DTSP), is still an open issue. To solve DTSP, several methods exist using for example Ant Colony Optimization (ACO) [1,2,12], Genetic Algorithm (GA) [3,9], Integer Linear Program (ILP) solvers [22,23] or Self-Organizing Map (SOM) [7,26]. ACO obtains good performance for DTSP where cities appear and disappear, but are completely inappropriate for moving targets.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic Algorithms (GA) [3,9] offer a sub-optimal trajectory with the most reduced time or distance to collect targets, but require a long processing time and there lack of approaches in the literature for attributing targets to a multi-agent system (MAS). ILP solvers [22,23] and SOM [7,26] allow to obtain results with less calculation time. However, ILP solvers require specific configurations such as targets moving with zero accelerations and following linear paths while SOM's trajectories for moving targets are not optimal.…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm‐based multiple moving target reaching using a fleet of sailboats

Viel

Ulysse

Wan

et al. 2019

IET cyber-systems robotics

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This paper addresses the problem of Dynamic Traveling Salesman Problem (DTSP) for a multi-agent system using a fleet of sailboats. A genetic algorithm (GA) is proposed which attributes to each agent a varying number of targets to be collected. GA allows to obtain a suboptimal solution in the shortest time possible. Moreover, this paper adapts it to the specific problem involving a fleet of sailboats, which is a challenging task with comparison to Unmanned Surface Vehicle (USV) or motorized vehicles in terms of the propulsion. Therein motors can be flexibly controlled while sailboat movements are constrained by available wind direction and speed. Thus the method takes into account wind conditions at various locations of the sailboat. Simulation results demonstrate the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm‐based multiple moving target reaching using a fleet of sailboats

Viel

Ulysse

Wan

et al. 2019

IET cyber-systems robotics

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“…It can be solved "offline" and new data is then integrated into the offline algorithm at runtime by a moving horizon approach. For a detailed description of the application we refer to Stieber et al (2014).…”

Section: Introductionmentioning

confidence: 99%

“…Based on different ways of integrating time into the model formulation, four different variants are presented and compared regarding performance. Two model approaches have already been published by Stieber et al (2014), the time-discrete model TD and the time-continuous model TC. The latter one concerning continuous times, is the most precise model variant.…”

Section: Introductionmentioning

confidence: 99%

Dealing with time in the multiple traveling salespersons problem with moving targets

Stieber

Fügenschuh

2020

Cent Eur J Oper Res

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The multiple traveling salespersons problem with moving targets is a generalization of the classical traveling salespersons problem, where the targets (nodes or objects) are moving over time. Additionally, for each target a visibility time window is given. The task is to find routes for several salespersons so that each target is reached exactly once within its visibility time window and the sum of all traveled distances of all salespersons is minimal. We present different modeling formulations for this TSP variant. The time requirements are modeled differently in each approach. Our goal is to examine what formulation is most suitable in terms of runtime to solve the multiple traveling salespersons problem with moving targets with exact methods. Computational experiments are carried out on randomly generated test instances to compare the different modeling approaches. The results for large-scale instances show, that the best way to model time requirements is to directly insert them into a formulation with discrete time steps.

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