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While there are optimal TSP solvers, as well as recent learning-basedapproaches, the generalization of the TSP to the Multiple Traveling SalesmenProblem is much less studied. Here, we design a neural network solution thattreats the salesmen, cities and depot as three different sets of varyingcardinalities. We apply a novel technique that combines elements from recentarchitectures that were developed for sets, as well as elements from graphnetworks. Coupled with new constraint enforcing output layers, a dedicatedloss, and a search method, our solution is shown to outperform all themeta-heuristics of the leading solver in the field.

Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks