Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length
Journal of the ACM (JACM)
Dynamic Programming Treatment of the Travelling Salesman Problem
Journal of the ACM (JACM)
The vehicle routing problem
2-Path Cuts for the Vehicle Routing Problem with Time Windows
Transportation Science
Time-Varying Travel Times in Vehicle Routing
Transportation Science
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
Vehicle routing problem with elementary shortest path based column generation
Computers and Operations Research
Restricted dynamic programming: A flexible framework for solving realistic VRPs
Computers and Operations Research
Restricted dynamic programming: A flexible framework for solving realistic VRPs
Computers and Operations Research
Perceptual control architecture for cyber-physical systems in traffic incident management
Journal of Systems Architecture: the EUROMICRO Journal
Modelling and solving for ready-mixed concrete scheduling problems with time dependence
International Journal of Computing Science and Mathematics
Survey of Green Vehicle Routing Problem: Past and future trends
Expert Systems with Applications: An International Journal
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Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic 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 congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.