The Traveling Salesman Problem and its Variations

Monnot, Jérôme; Toulouse, Sophie

doi:10.1002/9781119005353.ch7

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…The Travelling Salesman Problem (TSP) algorithm is a method that is most frequently used when solving the problem of route optimization, and it can be incorporated together with new methods that are suitable when there is a larger number of ports/cities, such as ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO), k-means clustering, shrink wrap algorithm and meta-heuristics [35,[49][50][51][52]. When solving the optimization problem, using TSP, the branch and bound and the nearest neighbor algorithm are the most frequently used, and they give, in comparison with other algorithms, better results [34][35][36][37][38][39][40][41]. This is why these two algorithms will be used in solving the feeder service optimization problem of the Adriatic Sea.…”

Section: Optimization Of the Feeder Service Modelmentioning

confidence: 99%

“…In accordance with all previously stated research, the main objective of this study is to find a model for selecting the hub port that can handle the feeder service in the Adriatic Sea, and to optimize the network of service seaports. This can be achieved by applying the method of multi-criteria decision making for selecting an acceptable hub port, and by using two different travelling salesman problem algorithms (TSP) for optimizing the feeder service route, which are most often used in similar research [34][35][36][37][38][39][40][41]. If the sub-hub system is being used, optimization can be achieved using the minimum spanning tree algorithm (MST), which is most often used in similar types of research [42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

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Adriatic Sea Hub Ports Feeder Service Optimization Using Multi-Criteria Decision-Making Methods

et al. 2021

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From a scientific viewpoint, as well as from the perspective of navigation practice, it is clear that the Adriatic Sea feeder service is relatively underdeveloped. Hence, the objective of this study is to suggest a model for selecting the hub port and to optimize the network of seaports engaged in the feeder service. Accordingly, an appropriate hub port has been identified through the methods of multi-criteria decision making and expert assessment, and the optimum shipping route has been calculated by applying the travelling salesman algorithm (TSA). In order to analyze whether there is a possibility of obtaining better optimization results, an integration of a sub-hub port system is suggested. Optimization has been achieved by applying a minimum spanning tree algorithm (MST) and a combination of these algorithms. The proposed methodology for selecting the hub port, sub-hub port and optimizing the feeder network can be implemented globally. The practical application of the achieved model would result in cost minimization, owing to shorter shipping routes or a combination of different transportation means (feeders).

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Section: Optimization Of the Feeder Service Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adriatic Sea Hub Ports Feeder Service Optimization Using Multi-Criteria Decision-Making Methods

et al. 2021

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“…The discrete environment model is mostly used in the path planning process for single-UAV persistent surveillance, turning the problem into classical search problems such as the traveling salesperson problem [19]. Several methods have been used for environment decomposition, such as approximate cellular decomposition, constrained Delaunay triangulation (CDT), rectilinear partition, and identical regular hexagonal cells [20–22].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment

Jing

Wang

et al. 2019

Computational Intelligence and Neuroscience

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The persistent surveillance problem has been proved to be an NP hard problem for multiple unmanned aerial vehicle systems (UAVs). However, most studies in multiple UAV control focus on control cooperative path planning in a single swarm, while dynamic deployment of a multiswarm system is neglected. This paper proposes a collective control scheme to drive a multiswarm UAVs system to spread out over a time-sensible environment to provide persistent adaptive sensor coverage in event-related surveillance scenarios. We design the digital turf model to approximate the mixture information of mission requirements and surveillance reward. Moreover, we design a data clustering-based algorithm for the dynamic assignment of UAV swarms, which can promote workload balance, while also allowing real-time response to emergencies. Finally, we evaluate the proposed architecture by means of simulation and find that our method is superior to the conventional control strategy in terms of detection efficiency and subswarm equilibrium degree.

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“…It can be presented as a sub‐problem in many transportations and logistics applications, for example finding the shortest path for vehicles or the fastest path to collect data using a robot. TSP can be divided into a multitude of problems [18, 19]. However, the literature on TSP is still sparse with regards to moving targets, like in fishing [11], surveillance [20], or in military applications [14, 21].…”

Section: Related Workmentioning

confidence: 99%

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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The Traveling Salesman Problem and its Variations

Cited by 9 publications

References 32 publications

Adriatic Sea Hub Ports Feeder Service Optimization Using Multi-Criteria Decision-Making Methods

Adriatic Sea Hub Ports Feeder Service Optimization Using Multi-Criteria Decision-Making Methods

Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment

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

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