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Of course the traveling salesman problem is combinatorial optimization, and of course Uber, Lyft, Sidecar, etc. have such problems, as has long been the case for dial a ride.

And, for the case of trips regularly planned, e.g., using one car to get the same several people to work each day, would have a deterministic problem. Of course, part of the problem would be which people shared which car.

Otherwise there is something of a probabilistic problem where the driver gets a travel request at some random time for some random origin and destination and has to decide what to do, e.g., accept the offer or not and how to fit the offer in with what else he is doing.

Might be a lot of work, but might save some money and/or give better service to the customers.

At one time, my Ph.D. advisor wanted me to work on dial a ride scheduling. But I already had a problem I'd made good progress on and used that for my dissertation instead! One reason: For the problem I used, I could claim to have a solution that was optimal in a quite strong sense, but for dial a ride any solution would be only a big mess probabilistically, some big mess that was just heuristic, and that could be evaluated only empirically. Bummer.

Now, I also have another problem! So, anyone who wants to work on combinatorial optimization and hope to get interest from Uber, Lyft, Sidecar, etc. -- go for it!




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