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This paper presents integrated models of probabilistic vehicle routing and scheduling model with time windows and dynamic traffic simulation. The probabilistic model incorporates the uncertainty of travel times. After applying the models to test road network, incorporating the uncertainty of travel times using the models not only allows freight carriers to reduce their total costs, but also improves the environment in terms of decreasing CO₂ emissions. As well, the paper investigated the effects and profitability of co-operative freight transport systems using a simulation model based on forecast vehicle routing and scheduling model. Results showed cooperative freight transport systems can lead to a substantial reduction in total delivery costs and total travel times within the whole road network.

In this chapter, four bi-objective vehicle routing problems are considered. Weighted-sum approach optimization models are formulated with the use of mixed-integer programming. In presented optimization models, maximization of capacity of truck versus minimization of utilization of fuel, carbon emission, and production of noise are taken into account. The problems deal with real data for green logistics for routes crossing the Western Pyrenees in Navarre, Basque Country, and La Rioja, Spain.

Heterogeneous fleet of trucks is considered. Different types of trucks have not only different capacities, but also require different amounts of fuel for operations. Consequently, the amount of carbon emission and noise vary as well. Modern logistic companies planning delivery routes must consider the trade-off between the financial and environmental aspects of transportation. Efficiency of delivery routes is impacted by truck size and the possibility of dividing long delivery routes into smaller ones. The results of computational experiments modeled after real data from a Spanish food distribution company are reported. Computational results based on formulated optimization models show some balance between fleet size, truck types, and utilization of fuel, carbon emission, and production of noise. As a result, the company could consider a mixture of trucks sizes and divided routes for smaller trucks. Analyses of obtained results could help logistics managers lead the initiative in environmental conservation by saving fuel and consequently minimizing pollution. The computational experiments were performed using the AMPL programming language and the CPLEX solver.