Daily traffic congestions form major problems for businesses such as logistical service providers and distribution firms. They cause late arrivals at customers and additional hiring costs for the truck drivers. The additional costs of traffic congestions can be reduced by taking into account and avoid well-predictable traffic congestions within off-line vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestions, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other congestion avoidance strategies in off-line vehicle route plans on the performance of these plans in reality. For this purpose, we develop a set of VRP instances on real road networks, and a speed model that inhabits the main characteristics of peak hour congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestions are accounted for off-line. On top of that, almost 70% of the extra duty times caused by the traffic congestions can be eliminated by clever avoidance strategies.
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