“Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?”
This is known as the “Traveling Salesman Problem” (TSP), one of the classes of problems in computational complexity theory which is designated as “NP-Hard”. Due to this incredible level of complexity, the logistics industry relies on narrow AI to produce close approximations rather than attempting to calculate the exact answer. In this problem’s simplest and most popular form many such systems have gotten very good at finding answers that were either exact or within less than 1% of the exact optimal answer. However, once you step into the real world dozens or even hundreds of additional factors may arise, which leads to much more messy approximations. The subsequent impact of these messy approximations is felt in the transportation of people, products, and produce.