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I take that it's a 'kind' of decision tree or random forest with gradient boosting, so usually it 'can' run faster on a CPU if optimized than on a GPU (if I'm not mistaken). That's at least what I get from BigML's offering.

edit: Can you provide insight?

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It is not decision tree or any machine learning algorithm.

It is a local search algorithm that probably has simulated annealing or tabu search as a metaheuristic.

Although, they probably segment the deliveries to some common starting points and the problem size is reduced significantly - maybe to around thousand orders per starting point.

Research can easily optimize thousands of deliveries very effectively.

wow I'm intrigued and haven't even heard of tabu search before you mentioned it, thank you very much! You surely are my hero today loserboss =)

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