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D ue to the growing concern over environmental issues, regardless of whether companies are going to voluntarily incorporate green policies in practice, or will be forced to do so in the context of new legislation, change is foreseen in the future of transportation management. Assigning and scheduling vehicles to service a pre-determined set of clients is a common distribution problem. Accounting for time-dependent travel times between customers, we present a model that considers travel time, fuel, and CO 2 emissions costs. Specifically, we propose a framework for modeling CO 2 emissions in a time-dependent vehicle routing context. The model is solved via a tabu search procedure. As the amount of CO 2 emissions is correlated with vehicle speed, our model considers limiting vehicle speed as part of the optimization. The emissions per kilometer as a function of speed are minimized at a unique speed. However, we show that in a timedependent environment this speed is sub-optimal in terms of total emissions. This occurs if vehicles are able to avoid running into congestion periods where they incur high emissions. Clearly, considering this trade-off in the vehicle routing problem has great practical potential. In the same line, we construct bounds on the total amount of emissions to be saved by making use of the standard VRP solutions. As fuel consumption is correlated with CO 2 emissions, we show that reducing emissions leads to reducing costs. For a number of experimental settings, we show that limiting vehicle speeds is desired from a total cost perspective. This namely stems from the trade-off between fuel and travel time costs.

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Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the traffic congestion is captured based on queueing theory in an analytical way and applied to the VRP problem. In this paper, we introduce the variability in the traffic flows into the model. This allows for an evaluation of the routes based on the uncertainty involved. Different experiments show that the risk taking/avoiding behaviour of the planner can be taken into account during optimization. As more weight is contributed to the variability component, the resulting optimal route will be slightly slower, but more reliable. The solution quality in terms of the 95 th -percentile of the travel time distribution (assumed lognormal) will also improve.