Replacing a fossil fuel-powered car with an electric model can halve greenhouse gas emissions over the course of the vehicle's lifetime and reduce the noise pollution in urban areas. In green logistics, a well-scheduled charging ensures an efficient operation of transportation and power systems and, at the same time, provides economical and satisfactory charging services for drivers. This paper presents a taxonomy of current electric vehicle charging scheduling problems in green logistics by analyzing its unique features with some typical use cases, such as space assignment, routing and energy management; discusses the challenges, i.e., the information availability and stakeholders' strategic behaviors that arise in stochastic and decentralized environments; and classifies the existing approaches, as centralized, distributed and decentralized ones, that apply to these challenges. Moreover, we discuss research opportunities in applying market-based mechanisms, which shall be coordinated with stochastic optimization and machine learning, to the decentralized, dynamic and data-driven charging scheduling problems for the management of the future green logistics.
翻译:以电模型取代化石燃料驱动的汽车,可以在汽车使用寿命期间将温室气体排放减半,并减少城市地区的噪音污染。在绿色物流中,计划有序的收费确保运输和电力系统的高效运行,同时为司机提供经济和令人满意的收费服务。本文通过分析当前电动车辆的独特特点,分析其特殊特点,如空间分配、路由和能源管理等典型用途案例,从而对绿色物流中的排期问题进行分类;讨论在零碎和分散环境中出现的挑战,即信息可得性和利益攸关方的战略行为;将适用于这些挑战的现有方法分类为集中、分散和分散的方法。此外,我们讨论了应用基于市场的机制方面的研究机会,这些机制应与随机优化和机器学习相协调,以便解决分散、动态和数据驱动的排期问题,从而管理未来的绿色物流。