Planning algorithms are widely used in logistics and control. They can help schedule flights and bus routes, guide autonomous robots, and determine control policies for the power grid, among other things.
In recent years, planning algorithms have begun to factor in uncertainty -- variations in travel time, erratic communication between autonomous robots, imperfect sensor data, and the like. That causes the scale of the planning problem to grow exponentially, but researchers have found clever ways to solve it efficiently.