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With the fastest consumer demand growth, the increasing customer’s demands trend to multivarieties and small-batch and the customer requires an efficient distribution planning. How to plan the vehicle route to meet customer satisfaction of mass distribution as well as reduce the fuel consumption and emission has become a hot topic. This paper proposes a two-phase optimization method to handle the vehicle routing problem, considering the customer demands and time windows coupled with multivehicles. The first phase of the optimization method provides a fuzzy hierarchical clustering method for customer grouping. The second phase formulates the optimization en-group vehicle routing problem model and a genetic algorithm to account for vehicle routing optimization within each group so that fuel consumption and emissions are minimized. Finally, we provide some numerical examples. Results show that the two-phase optimization method and the designed algorithm are efficient.

The microscopic stripe pillar is one of the most frequently adopted building blocks for hydrophobic substrates. However, at high temperatures the particles on the droplet surface readily evaporate and re-condense on the pillar sidewall, which makes the droplet highly unstable and undermines the overall hydrophobic performance of the pillar. In this work, molecular dynamics (MD) simulation of the simple liquid at a single stripe pillar edge defect is performed to characterize the droplet's critical wetting properties considering the evaporation-condensation effect. From the simulation results, the droplets slide down from the edge defect with a volume smaller than the critical value, which is attributed to the existence of the wetting layer on the stripe pillar sidewall. Besides, the analytical study of the pillar sidewall and wetting layer potential field distribution manifests the relation between the simulation parameters and the degree of the droplet pre-wetting, which agrees well with the MD simulation results.