The traveling salesman algorithm is a popular challenge for students and software developers. But travel managers and travelers need better solutions than theoretical round trip optimization. They need practice proven answers for daily challenges.

Flexible multi destination travel


From a travel managers as well as a travelers perspective, the ‘traveling salesman’ question is less academic. They simply need algorithms to optimize travel time and -cost in multi-destination trips.

Reality shows that business travelers need the ability for less strict algorithms then the theoretical approach. But why is that the case?

Multi destination travel can have open ends


The academic traveling salesman is a slowpoke and wants to end where he started. On normal travel portals this is more or less fuzzy as they only handle travel hubs, not addresses. It may e.g. mean that you fly from Los Angeles Airport (LAX) to Berlin Tegel (TXL) and back.

For a multimodal door-to-door engine like ours it is much more concrete as we built itineraries from where you really start, not from the next big transport hub. And you might start from your home in Los Angeles and return to your office in Los Angeles as final destination.

Even though origin an destination might be nearby it is not a closed trip. As we can handle multi destination with open ends there is not limitation for open or closed trips. Each trip step is multimodal door-to-door. And you can insert breaks or stopovers if you want them.

Customers overrule route optimum


An algorithm can optimize a given pattern, like shortest distance, best price, fewest stopovers etc. but business travelers are mostly driven by deadlines and customers calendars.

If you e.g. have to visit 8 customers in an week and there would be an ideal route in between to close a roundtrip at best conditions, the result will be worthless if your customers schedules make you travel in zigzags.

Still, an algorithm can optimize what is left in between the zigzags, so this is what we do for you in case your appointment dates ruin an overall optimum.

An ideal trip might be slow and interrupted


Finally the traveler is the expert who can take planned or spontaneous decisions on top of a mathematical perfect itinerary based on his experience and work decisions.

Sample:
Imagine a trip with 6 meetings on 4 days. The algorithms shows you an option to finish everything at best price in 3 days.

But the traveler knows that he will need time to wrap up results in between to deliver information to the clients from the first workshops.

And it might even be that the next clients schedule urges the traveler to insert a break in his multi destination travel itinerary to have a meeting nearby, but one day later.

What we do for you


Our approach is always to use clever algorithms to prepare a great base to start from. The user can always refine the results based on his knowledge and preferences. And in the final round we check if we can make clever suggestions after analyzing the user refined itinerary.

On top of that, our CRM integrations enrich the abilities even more.