论文标题
随机车队混合优化：评估城市物流中的电动性
Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics
论文作者
论文摘要
在本文中，我们研究了优化提供城市货运物流服务公司拥有的电动和常规车辆的大小和混合车队的问题。在战略计划阶段考虑不确定的客户要求。这些请求在每个操作期开始之前都会揭示。在操作层面，建议了一种新的车辆功耗模型。除了机械功耗外，该模型还考虑了机舱气候控制能力，该电源取决于环境温度和辅助功率，该功率解释了外部设备所吸收的能量。我们提出了随机车队大小的问题，并将优化作为一个两阶段的随机程序，并提出了基于样本平均近似启发式方法来解决它的问题。在每个操作期间，使用自适应大型邻里搜索算法来确定运营决策和相关成本。该方法的适用性通过城市物流服务中的两个案例研究来证明。
In this paper, we study the problem of optimizing the size and mix of a mixed fleet of electric and conventional vehicles owned by firms providing urban freight logistics services. Uncertain customer requests are considered at the strategic planning stage. These requests are revealed before operations commence in each operational period. At the operational level, a new model for vehicle power consumption is suggested. In addition to mechanical power consumption, this model accounts for cabin climate control power, which is dependent on ambient temperature, and auxiliary power, which accounts for energy drawn by external devices. We formulate the problem of stochastic fleet size and mix optimization as a two-stage stochastic program and propose a sample average approximation based heuristic method to solve it. For each operational period, an adaptive large neighborhood search algorithm is used to determine the operational decisions and associated costs. The applicability of the approach is demonstrated through two case studies within urban logistics services.
