Cargo transportation models analysis using multi-agent adaptive real-time truck scheduling system

Different models of transportation are analyzed based on simulation of statistically significant flows of orders. Models include the rigid ones, where trucks return back to their garage after each trip, and more flexible ones, where trucks wait for new orders at the unloading positions, where they are allowed to delay trips, and finally where orders can be adaptively rescheduled “on the fly” in real-time and the schedule of each truck can be changed individually while executed. Diagrams are presented which show how profit of all trucks changes in time for each model. The profit dependencies on the number of trucks are built and analyzed. They show that it is possible to execute the same number of orders with less number of trucks using adaptive scheduling.