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The Fuel-Efficient Platooning of Heavy Duty Vehicles by Mathematical Programming and Genetic Algorithm

机译:通过数学规划和遗传算法的重型车辆燃油排放

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As fuel accounts for a significant proportion of the total operational cost of heavy duty vehicles (HDVs) and to alleviate the environmental impacts, companies are seeking for novel practical methods to reduce fuel consumption. Platooning is an effective approach in which a string of HDVs driving close behind each other is formed. This reduces the aerodynamic drag leading to a reduction in the overall resistive force on vehicles which can provide an amount of fuel-saving in each of the following vehicles. These trivial reductions result in a considerable decrease in the total fuel consumption corresponding to all the vehicles. In this paper, we propose a mathematical model for the fuel-efficient platooning problem with a deadline for each vehicle (truck) to reach its destination by then. We create a graph partly based on the German road network considering only 20 important cities and solve 50 instances including 10 to 50 trucks. The small samples are solved by the LINDO solver in real time but as the problem has a high computational complexity, a Genetic Algorithm is applied to obtain fast solutions of a good quality for larger instances. The final results show a satisfactory fuel-saving in all of the cases.
机译:由于燃料占重型车辆(HDV)总运营成本的大量比例,并减轻环境影响,公司正在寻求新的实用方法来降低燃料消耗。排列是一种有效的方法,其中形成驱动彼此靠近靠近的HDV的串。这降低了空气动力学阻力,导致车辆上的整体电阻力减少,这在以下每个车辆中可以提供省油量。这些琐碎的减少导致与所有车辆相对应的总燃料消耗的显着降低。在本文中,我们向每辆车(卡车)截止到达目的地的难度排列问题的数学模型。我们根据德国道路网络组成了一个图表,考虑了20个重要城市,并解决了50个实例,包括10到50辆卡车。小型样品在Lindo求解器实时解决,但由于该问题具有高计算复杂性,因此应用了遗传算法以获得更大的实例的良好质量的快速解决方案。最终结果在所有病例中都显示出令人满意的燃料。

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