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Solving for X: Once the FAA clears the way for the Eclipse 500, Iacobucci will get to see how good his models really are.

 

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"This is way nastier than any of the other airline-scheduling work we've ever done," says Georgia Tech professor George Nemhauser, whose PhD students have been helping to map the scope of DayJet's mountain-sized scheduling dilemma. "You can think of this as a traveling-salesman problem with a million cities, and that's a problem DayJet has to solve every day."

Tapping into the school's computing power, Nemhauser and his students have figured out how to calculate a near-perfect solution for 20 planes in a few seconds' worth of computing time and a solution for 300 planes in 30 hours. But as impressive as that is, in the real world, it's not nearly enough. That's because in order for DayJet's reservations system to succeed, Iacobucci and company need an answer and a price in less than five seconds, the limit for anyone conditioned to Orbitz or Expedia. Because DayJet has no preset schedule--and because overbooking is out of the question (DayJet will fly two pilots and three passengers maximum)--any request to add another customer to a given day's equation requires its software to crunch the entire thing again.

One of Iacobucci's oldest pals and investors, former Microsoft CFO and Nasdaq chairman Mike Brown, pointed him toward a shortcut--a way to cheat on the math. Brown had retired with his stock options to pursue his pet projects in then bleeding-edge topics such as pattern recognition, artificial intelligence, nonlinear optimization, and computational modeling. His dabblings led him first to Wall Street, where he invested in a trading algorithm named FATKAT and eventually to Santa Fe, New Mexico, ground zero for complexity science.

Iacobucci says 80% of his revenues will come from travelers who would otherwise drive. DayJet, in other words, is creating a market where none existed, an astonishing mathematical feat.

Invented by scientists at the nearby Los Alamos National Laboratory in the 1980s, complexity science is a gumbo of insights drawn from fields as diverse as biology, physics, and economics. At its core is the belief that any seemingly complex and utterly random system or phenomenon--from natural selection to the stock market--emerges from the simple behavior of thousands or millions of individuals. Using computer algorithms to stand in for those individual "agents," scientists discovered they could build fantastically powerful and detailed models of these systems if only they could nail down the right set of rules.

When Brown arrived in town in the late 1990s, many of the scientists-in-residence at the Santa Fe Institute--the serene think tank dedicated to the contemplation of complexity--were rushing to commercialize their favorite research topics. The Prediction Co. was profitably gaming Wall Street by spotting and exploiting small pockets of predictability in capital flows. An outfit called Complexica was working on a simulator that could basically model the entire insurance industry, acting as a giant virtual brain to foresee the implications of any disaster. And the BiosGroup was perfecting agent-based models that today would fall under the heading of "artificial life."

By the time Iacobucci mentioned his logistical dilemma to Brown in 2002, however, most of Santa Fe's Info Mesa startups were bobbing in the dotcom wreckage. But Brown knew that Bios had produced astonishingly elegant solutions a few years earlier by creating virtual "ants" that, when turned loose, revealed how a few false assumptions or bottlenecks could throw an entire system out of whack. A model Bios built of Southwest's cargo operations, for example, cost $60,000 and found a way to save the airline $2 million a year.

Brown proposed that Iacobucci supplement his tool kit with a healthy dose of complexity science. Iacobucci was already hard at work building an "optimizer" program that employed nonlinear algorithms and other mathematical shortcuts to generate scheduling solutions in seconds. But what he really needed, Brown suggested, was an agent-based model (ABM) that would supply phantom traveling salesmen to train the optimizer. Without it, he'd essentially be guessing at the potential number and behavior of his future customers. "Eddy took no convincing," Brown says. "He was telling me, 'Get some guys down here and let's do this.'"

Brown dug up the Ant Farmers, a pair of Bios refugees and expert modelers named Bruce Sawhill and Jim Herriot. Sawhill had been a theoretical physicist at the Santa Fe Institute, while Herriot had been a member of the original team that invented Java at Sun Microsystems. Together, they're DayJet's own Mutt and Jeff, with Herriot playing congenial science professor and Sawhill his mischievous sidekick.