Why would you give up just because something is NP hard if there are good algorithms to approximate a solution, and an approximate but good solution is useful?
Yeah, there are many problems which are np-hard in theory, but then realistic cases give you way more constraints that make them solvable. So many hard graph problems become way simpler when applied to real maps, because you know that if you start getting away from something, your minimum remaining distance grows. But on an abstract graph there's no real mapping to our dimensions.
There's no "information you wanted" in the plain np-hard version of traveling salesman for example. There's only cost. My point was that things get easier if you have the extra information and aren't solving the plain version anymore.