Model predictive based approach to solve DVRP with traffic congestion

Vehicle routing problem (VRP) is one of the most important contexts that has attracted engineers' attention nowadays. Using static VRP gives us improper solutions in the real world because of numerous uncertainties and challenges such as traffic congestion, car crashes, and adding and/or countermand orders. To overcome the challenges, we propose a dynamic vehicle routing problem (DVRP) solution considering traffic congestion, which uses a distributed cooperative predictive approach. The normal distribution is used for modeling traffic congestion. Also, we consider multi-route between each node. After specific sample time, the situation will be checked; then, as the occasion arises, we use capacitated clustering (CC) and binary integer programming to solve DVRP in view of the predictive method. Experiments present that the proposed algorithm reduces the final cost up to 8% compared to the static VRP.