Visual analytics approach to user-controlled transportation scheduling
We consider the problem of scheduling the transportation of items of diverse categories from multiple geographically distributed sources to multiple destinations, e.g. evacuation from a disaster-affected area. The problem is geographic in nature and involves multiple criteria, some being hard to formalize. It cannot be solved by purely automatic methods and requires a synergy of human and computer. We combine a meta-heuristic scheduling tool based on a genetic algorithm with techniques from the field of visual analytics allowing the user to evaluate tool results and direct its work.