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A Hybrid Evolutionary Algorithm for Smart Freight Delivery with Electric Modular Vehicles

机译:电动模块化车辆智能货运的混合进化算法

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This paper proposes a hybrid evolutionary algorithm for smart urban freight distribution using electric modular vehicles. The hybridization of evolutionary algorithms has recently shown promising results in vehicle routing problems with time windows. We propose here to test this technique on a new problem of routing electric vehicles with limited ranges which have the particularity of being modular. This means that the studied vehicles are composed of several modules which can be released at dedicated locations to go on performing the tours in an optimized way. For big instances of the problem which is NP-Hard, metaheuristics and in particular those involving evolutionary computation are suitable. Therefore, in this study, we propose to address the problem with the hybridization of the genetic algorithm. We use the Best Route crossover operator in combination of a Variable Neighborhood Descent algorithm for the mutation phase. An experimental study on benchmark instances from the literature shows the performance and effectiveness of the resulting evolutionary based algorithm.
机译:本文提出了一种使用电动模块化车辆的智能城市货运分配的混合进化算法。进化算法的混合最近在具有时间窗的车辆路径问题中显示出令人鼓舞的结果。我们在这里建议在路由有限范围的电动汽车的新问题上测试该技术,该问题具有模块化的特殊性。这意味着所研究的车辆由几个模块组成,这些模块可以在专用位置释放,以便以优化的方式继续执行巡回演出。对于NP-Hard问题的大型实例,元启发法,尤其是涉及进化计算的元启发法是合适的。因此,在这项研究中,我们建议通过遗传算法的混合来解决该问题。对于突变阶段,我们将“最佳路线”交叉算子与可变邻域下降算法结合使用。从文献中对基准实例进行的实验研究表明了所得基于进化算法的性能和有效性。

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