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Publik·10 anggota
Jose Rodriguez
Jose Rodriguez

Free Interactive Traveling Salesman Problem Solver For Any Location On Map !FREE!


This is the problem we have solved.The optimal tour has length 45,495,239 meters.To be clear, our main result is that there simply does not exist any pub tour that is even one meter shorter (measuring the length using the distances we obtained from Google) than the one produced by our computation.It is the solution to a 24,727-city traveling salesman problem (TSP).




Free Interactive Traveling Salesman Problem Solver For Any Location On Map



Use this tool to find the most efficient route between a start location and end location and way points in between. This tool will rearrange the order of the way points to produce the most efficient route. This is also known as the traveling salesman problem.


For example, consider the graph shown in the figure on the right side. A TSP tour in the graph is 1-2-4-3-1. The cost of the tour is 10+25+30+15 which is 80. The problem is a famous NP-hard problem. There is no polynomial-time know solution for this problem. The following are different solutions for the traveling salesman problem.


The first classic VRP is known as the traveling salesman problem (TSP), which originated in the early 1800s and became widespread in the days when door-to-door salesmen peddled vacuum cleaners and encyclopedias. With time, VRP was categorized into much more sophisticated tasks involving large chunks of data.


Now with the 2,450 landmark-landmark distances, our next step was to approach the task as a traveling salesman problem: We needed to order the list of landmarks such that the total distance traveled between them is as small as possible if we visited them in order. This means finding the route that backtracks as little as possible, which is especially difficult when visiting Florida and the Northeast.


Clearly, we need a smarter solution if we want to take this epic road trip in our lifetime. Thankfully, the traveling salesman problem has been well-studied over the years and there are many ways for us to solve it in a reasonable amount of time.


The traveling salesman problem might be described as follows: Find the shortest route for a salesman starting from a given city, visiting each of a specified group of cities, and then returning to the original point of departure. More generally, given an n by n symmetric matrix D = (dij), where dij represents the "distance" from i to j, arrange the points in a cyclic order in such a way that the sum of the dij between consecutive points is minimal. Since there are only a finite number of possibilities to consider, the problem is to devise a method of picking out the optimal arrangement which is reasonably efficient for fairly large values of n. Although algorithms have been devised for problems of similar nature, e.g., the optimal assignment problem, little is known about the traveling salesman problem. We do not claim that this note alters the situation very much; what we shall do is outline a way of approaching the problem that sometimes, at least, enables one to find an optimal path and prove it so.


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