论文标题
电动汽车旅行推销员与无人机的问题以及部分充电政策
Electric Vehicle Traveling Salesman Problem with Drone with Partial recharge Policy
论文作者
论文摘要
在（Zhu等人，2022年）中，它提出了一个无人机的电动汽车旅行推销员问题，同时假设电动汽车（EV）是电池电动车辆，可以在几分钟内在电池交换站中刷新能量。在本文中，假设EV是可以在充电站中部分充电的插件混合动力电动汽车，我们通过放松固定的时光充电假设来扩展（Zhu等，2022）中的工作。此问题被称为电动汽车旅行推销员与无人机的问题，其中有部分充电政策（EVTSPD-P）。 Montoya等人提出的这项技术，提出了一种三指数MILP公式，以使用线性和非线性充电函数求解EVTSPD-P，其中使用分段线性函数近似凹入时间态函数（SOC）函数。 （2017）和Zuo等。 （2019）。此外，提出了一种特殊设计的自适应大型邻里搜索（ALNS）元元素，其中包含约束编程（CP），以解决实用大小的EVTSPD-P问题实例。数值分析结果表明，提出的ALNS方法比可变邻域搜索更有效，并且在用十个节点求解实例时，平均最佳差距约为3％。此外，使用具有六线分段近似值的分段线性函数的平均成本比线性近似少10.8％。
In (Zhu et al., 2022), it proposes an electric vehicle traveling salesman problem with drone while assuming that the electric vehicle (EV) is a battery-electric vehicle whose energy could be refreshed in a battery swap station in minutes. In this paper, we extend the work in (Zhu et al., 2022) by relaxing the fixed-time-full-charge assumption, assuming that the EV is a plug-in hybrid electric vehicle that could be partially recharged in a charging station. This problem is named electric vehicle traveling salesman problem with drone with partial recharge policy (EVTSPD-P). A three-index MILP formulation is proposed to solve the EVTSPD-P with linear and non-linear charging functions where the concave time-state-of-charge (SoC) function is approximated using piecewise linear functions, a technique proposed in Montoya et al. (2017) and Zuo et al. (2019). Furthermore, a specially designed adaptive large neighborhood search (ALNS) meta-heuristic, which incorporates constraint programming (CP), is presented to solve EVTSPD-P problem instances of practical size. The numerical analysis results indicate that the proposed ALNS method is more efficient than variable neighborhood search and has an average optimality gap of about 3% when solving instances with ten nodes. Besides, using a piecewise linear function with a six-line-segments approximation has an average of 10.8% less cost than a linear approximation.
