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The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

机译:具有时间窗和充电站的电动车队规模和混合车辆路径问题

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摘要

Due to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labeling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach.
机译:由于电动汽车领域的新法规和进一步的技术进步,研究界面临着将基于电能的限制纳入车辆路径问题的新挑战。这些限制之一是有限的电池容量,这使绕行到充电站成为必要,因此需要有效的行程计划机制以维持电动车辆与传统车辆相比的竞争力。我们介绍了带有时间窗和充电站的电动车队规模和混合动力车辆路径问题(E-FSMFTW),以建模有关车队组成和实际车辆路线(包括选择充电时间和地点)的决策。可用的车辆类型在其运输能力,电池尺寸和购置成本方面有所不同。此外,我们考虑了客户所在位置的时间窗口,这是现实世界中路由和计划问题的常见且重要的约束条件。我们通过分支机构和价格的方式解决了这个问题,并提出了一种混合启发式方法,该方法将自适应大邻域搜索与嵌入式本地搜索和标记过程结合在一起进行强化。通过使用精确方法也为E-FSMFTW和现有的单个车辆类型基准解决了一组新创建的基准实例,我们证明了该方法的有效性。

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