The district was spending more than $116 million - more than 10 percent of its budget - on transportation.Īnd so…Martin, his MIT colleague Arthur Delarue and their advisor, Dimitris Bertsimas, put their computer to the task…to find more cost-effective routes….and they came up with an option that the district was happy with! NARR: In 2017, when Chang was speaking, the district had more than 600 buses on the road, and having buses on the road cost money. A kickoff competition for computer scientists to help put hundreds of buses on more efficient routes and save the district millions of dollars.ĬHANG: Boston, as you all know, is a very difficult city to navigate, and it has a very high cost of living…and so, because of that, Boston Public Schools spends five times more per student than a typical school our size. And he’s speaking at a kickoff for a competition that BPS held that year. He was the superintendent at Boston Public Schools in 2017. Tommy CHANG: We have the second highest transportation cost per student in the country. It certainly caught the eye of Boston Public Schools…a district that had…a really hairy problem on its hands. This piece of technology….which he drew up with a fellow grad student at MIT…attracted attention. You see.this machine… could sort through 1 novemtrigintillion different route scenarios.
NARR: That’s the sound of Martin doing the calculations in his head. MARTIN: So yeah, the number of possibilities would be of the order of…ummm… It turns out, Martin ended up building a machine that could quickly plow through a pretty incredible number of possible routes. NARR: That’s Sebastien Martin - he’s an assistant professor of operations at Kellogg…and he specializes in using math and AI to make transportation systems as efficient as possible. Sebastien MARTIN: As the number of cities grow, the time it takes for a computer to find the very best path sort of explodes. And here’s the kicker: Every city you add.shoots the number of possible routes up exponentially higher. But actually….this is something that computer scientists have thought about for more than a to find the shortest round trip for just 15 mean sifting through 87 BILLION possible round-trip scenarios. I know, it kind of sounds like something you’d do for extra credit in eighth grade math class. And goes like this…ready? Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? Essentially, what is the most efficient round trip you can take?