Of course, even the human balks at understanding the behavior of other humans. Powell showed me a sample diagram he’ll sometimes give to a roomful of trucking executives. “I’ve got a load that has to be picked up. I’ve got a driver 60 miles away. All I have to do is dispatch him and it’s done. I’ve got another driver who, once he unloads, will be about 30 miles away—should be in around midafternoon.” Do you take the sure thing, even if it consumes more miles? Or do you take the risk on the shorter trip, which might get in later? When he asks for a show of hands which choice is better, the response is typically mixed. “Driver B saves me miles. But he hasn’t arrived yet, and what if he doesn’t?” he says, his voice dropping to a conspiratorial whisper. “Different people will answer it differently. They’ll say, ‘I know that driver, he’ll make it.’ Welcome to real-world decision making.”
Transport optimization, then, is hard, and understanding how to implement it can be harder still. “One of the hardest things to teach a math analytics group,” Santilli tells me, “is the difference between a feasible solution and an implementable solution. Feasible just means it meets all the math constraints. But implementable is something the human can carry out.”
But optimization has succeeded in coming a very long way. Modeling has become much more sophisticated, a development that Powell outlines in his life’s work, a book called Approximate Dynamic Programming: Solving the Curses of Dimensionality. “You went to the math literature, and it was all toy problems. It was literally about 20 years before I could go to a whiteboard and say, you know what, I can actually write down the problem.” Mapping has improved dramatically. A few decades ago, the mapping service UPS bought literally had people calling businesses to ask them if they actually were where the data UPS had suggested they were. Early GPS maps were also flawed. “When we got some bad results early on,” says Ranga Nuggehalli, UPS’s principal scientist, “we didn’t know whether it was because the algorithm was so bad, or our data was so bad.” It was the latter.
- Tom Vanderbilt is the author of Traffic: Why We Drive the Way We Do (and What It Says About Us)
Transport optimization, then, is hard, and understanding how to implement it can be harder still. “One of the hardest things to teach a math analytics group,” Santilli tells me, “is the difference between a feasible solution and an implementable solution. Feasible just means it meets all the math constraints. But implementable is something the human can carry out.”
But optimization has succeeded in coming a very long way. Modeling has become much more sophisticated, a development that Powell outlines in his life’s work, a book called Approximate Dynamic Programming: Solving the Curses of Dimensionality. “You went to the math literature, and it was all toy problems. It was literally about 20 years before I could go to a whiteboard and say, you know what, I can actually write down the problem.” Mapping has improved dramatically. A few decades ago, the mapping service UPS bought literally had people calling businesses to ask them if they actually were where the data UPS had suggested they were. Early GPS maps were also flawed. “When we got some bad results early on,” says Ranga Nuggehalli, UPS’s principal scientist, “we didn’t know whether it was because the algorithm was so bad, or our data was so bad.” It was the latter.
- Tom Vanderbilt is the author of Traffic: Why We Drive the Way We Do (and What It Says About Us)
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