Powell’s biggest revelation in considering the role of humans in algorithms, though, was that humans can do it better. “I would go down to Yellow, we were trying to solve these big deterministic problems. We weren’t even close. I would sit and look at the dispatch center and think, how are they doing it?” That’s when he noticed: They are not trying to solve the whole week’s schedule at once. They’re doing it in pieces. “We humans have funny ways of solving problems that no one’s been able to articulate,” he says. Operations research people just punt and call it a “heuristic approach.”
In loading trucks for warehouses, some OR guys I know ran into the opposite problem- they encoded all the rules as constraints, found a solution, and it was way worse than what people were actually doing. Turns out it was because the people actually loading the trucks didn’t pay attention to whether or not the load was balanced on the truck, or so on (i.e. mathematically feasible was a harder set of constraints than implementable because the policy book was harder than the actuality).
(I also don’t think it’s quite fair to call the OR approach ‘punting’, since we do quite a bit of optimization using heuristics.)
The Travelling Salesman Problem
In loading trucks for warehouses, some OR guys I know ran into the opposite problem- they encoded all the rules as constraints, found a solution, and it was way worse than what people were actually doing. Turns out it was because the people actually loading the trucks didn’t pay attention to whether or not the load was balanced on the truck, or so on (i.e. mathematically feasible was a harder set of constraints than implementable because the policy book was harder than the actuality).
(I also don’t think it’s quite fair to call the OR approach ‘punting’, since we do quite a bit of optimization using heuristics.)