traveling-salesman

Dear OptaPlanner experts! I would like to use OptaPlanner (or a similar Open Source Java Framework) to optimize routes for a bicycle messenger service. Let's assume 5 messengers have to pick up 30 envelopes FROM a certain origin and deliver them TO a certain destination: X(FROM) Y(FROM) X(TO) Y(TO) envelope 1 13745 55419 13883 55756 envelope 2 8406 53246 13937 55854 envelope 3 15738 57396 35996 79499 envelope 4 12045 60418 19349 57118 envelope 5 13750 56416 35733 78403 envelope 6 13190 57068 11860 59749 envelope 7 15021 55768 14098 57379 envelope 8 11513 58543 11501 59683 envelope 9 12013

I've been tasked to write an implementation of the A* algorithm (heuristics provided) that will solve the travelling salesman problem. I understand the algorithm, it's simple enough, but I just can't see the code that implements it. I mean, I get it. Priority queue for the nodes, sorted by distance + heuristic(node), add the closest node on to the path. The question is, like, what happens if the closest node can't be reached from the previous closest node? How does one actually take a "graph" as a function argument? I just can't see how the algorithm actually functions, as code. I read the

I'm trying to solve the TSP with Branch and bound algorithm. I must build a matrix with costs but I have this problem: I have city with coordinates x and y. The cost of traveling is ceil(ceil(sqrt((x1-x2)^2+(y1-y2)^2))/v) + days spent in the city. V is speed. Days spent in the city depends from day when w comes to the city. For example if we arrived on Monday(t1) to city 1, we stay for 9 days but if we arrived on Tuesday, then we stay in the city for 4 days. x y t1 . t7 city 1. 79 -36 9 4 8 5 5 7 8 city 2. 8 67 6 9 2 1 9 9 1 city 3. 29 57 7 5 10 8 10 9 4 How can I solve this problem using

I am looking to discuss branch and bound solution for TSP with multiple visits.(that is every city needs to be visited atleast once , instead of just once) Edit: Removed the doubt as it was not relevant as pointed by Jitse. Now the question is more clear. Simply augment the graph by adding, for each pair of nodes A and B, an edge representing the shortest path from A to B. The Floyd-Warshall algorithm allows you to do this in O(n^3), which is much faster than any TSP algorithm. Once you've done this, use a standard TSP branch and bound technique. This site has some information from Applegate's

I would like to know what is the problem name for TSP w/o considering the way of going back to starting point and what is the algorithm to solve this. I looked into Shortest path problem but that is not what I am looking for, the problem only find the shortest path from 2 assigned points. But what I am looking for is the problem which we give n points and inputting only 1 starting point. Then, find the shortest path traveling all points exactly once. (end point can be any point.) I also looked into Hamiltonian path problem but it seems not to solve my defined problem but rather find whether

I have a problem that has been effectively reduced to a Travelling Salesman Problem with multiple salesmen. I have a list of cities to visit from an initial location, and have to visit all cities with a limited number of salesmen. I am trying to come up with a heuristic and was wondering if anyone could give a hand. For example, if I have 20 cities with 2 salesmen, the approach that I thought of taking is a 2 step approach. First, divide the 20 cities up randomly into 10 cities for 2 salesman each, and I'd find the tour for each as if it were independent for a few iterations. Afterwards, I'd