A Parallel Ring Method for Solving a Large-scale Traveling Salesman Problem

Bazylevych, Roman; Кузь, Б.; Kutelmakh, Roman; Dupas, Rémy; Prasad, Bhanu; Haxhimusa, Yll; Bazylevych, Lubov

doi:10.5815/ijitcs.2016.05.01

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…These methods continue to be developed. A team of scientists proposed a Parallel Ring Method for solving a largescale Traveling Salesman Problem (TSP), where a clustering algorithm produces a set of small TSP problems that can be executed in parallel to generate partial solutions [9]. In general, heuristic methods make it possible to obtain a solution, but not always an optimal one.…”

Section: Methodsmentioning

confidence: 99%

Modeling the sequence of part machining to minimize the time spent on the machining center, considering the working tool changing

Matviishyn

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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An algorithm for choosing a rational sequence for machining a part on a machine with a tool magazine is proposed using the drilling operations example. The algorithm is based on the analysis of all possible options and the calculation of the time spent on each of them. In this respect, the working tool (drill) change is considered, if it is necessary to switch to another machining type (drilling a different diameter hole). Three types of time costs are included in the calculation of the total machining time for one part: the duration of each operation, the duration of the transition to the next operation, and the duration of the working tool change. The result of the algorithm application is the sequence of machining the part with the least total time spent on transitions to the machining areas and the working tool changing.

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Section: Methodsmentioning

confidence: 99%

Modeling the sequence of part machining to minimize the time spent on the machining center, considering the working tool changing

Matviishyn

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…However, our research focus is different in that we focus on route optimization for everyday maintenance, rather than finding or prioritizing the roads to be repaired first or on urgent bases. In an earlier research carried out in 2007, which is not directly related to the route optimization rather TSP as discussed in [35] improves calculation time, but it does not tackle any maintenance efficiency (i.e., distance and cost). The research concluded that Decomposition algorithms allow a substantial decrease in the running time of the clustered TSP, especially for the large-scale problems.…”

Section: Related Workmentioning

confidence: 99%

HiCARE: Hierarchical Clustering Algorithm for Road Service Routing Enhancement

Alshammari,

Song,

Choi

2023

IEEE Access

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The potholes in the road cause substantial monetary and physical damage to the ongoing traffic. Also, it requires extensive maintenance, especially in areas where the temperature may go down below freezing point. One of the major causes of potholes is the rain or running water that is accumulated in the cracks and later on due to low temperature, changes into ice. Then, ice forms a larger volume for the same amount of water after expanding and causes cracks to expand and at some point become a pothole. They are also the cause of traffic congestion. Therefore, pothole repair needs urgent attention and is one of the routine road maintenance tasks. Kansas City is one of the cities with established social data networks for residents to request road services to mitigate the problem. Although the policymakers have not ignored the issue and rudimentary patching policies are in place, unfortunately, that does not provide efficient road maintenance routes. This paper proposes a Hierarchical Clustering Algorithm for Road Service Routing Enhancement (HiCARE). HiCARE is a practical framework, that makes use of open data in order to optimize the route for maintenance. The optimization considers the time and date of reported potholes, locations, traffic situations, weather conditions, type of patch or other repairs needed, and crew availability, to mention some. This research has characterized spatiotemporal pothole density by analyzing the past 16 years of pothole data from the Open Data KC 311 in Kansas City. HiCARE enhances the NP-hard Traveling Salesperson Problem (TSP) by classifying potholes into layers of clusters. HiCARE finds a cluster's shortest possible pothole route by identifying each cluster group's entrance and exit pothole points. Moreover, it modifies the final routes to skip any local minima. The empirical research and analysis indicate that HiCARE significantly reduces the traversing distance and is faster in computation time when compared to typical TSP heuristic algorithms for daily resolution scheduling.

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“…2 of 33 algorithm is very huge that solving the instance with 85900 nodes will take over 136 CPUyears by Concorde, which is a mature exact solver for TSPs [20]. Intelligence algorithms are inspired by the nature world and have high capabilities to approximate the global optimal for optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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A Parallel Ring Method for Solving a Large-scale Traveling Salesman Problem

Cited by 5 publications

References 30 publications

Modeling the sequence of part machining to minimize the time spent on the machining center, considering the working tool changing

Modeling the sequence of part machining to minimize the time spent on the machining center, considering the working tool changing

HiCARE: Hierarchical Clustering Algorithm for Road Service Routing Enhancement

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

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