论文标题
绿色车辆路线问题：最新的和未来的方向
Green Vehicle Routing Problem: State of the Art and Future Directions
论文作者
论文摘要
绿色车辆路线问题（GVRP）旨在考虑减少温室气体排放，同时路由车辆。它可以通过采用替代燃料汽车（AFV）或舰队中现有的常规化石燃料汽车。 GVRP还考虑了运输和物流中的环境可持续性。我们批判性地回顾了GVRP的几种变体和专业化，以解决与充电，拾取，交付和能源消耗有关的问题。从GVRP的概念和定义开始，我们总结了GVRP出版物的关键要素和贡献者。之后，根据建议未来的研究方向和挑战，对每种绿色车辆路线的问题进行了审查。据观察，以前出版物的主要重点是运营级别的路由决策，而不是供应链问题。大多数出版物都使用元神经方法，同时忽略了新兴的机器学习方法。我们设想，除了机器学习，增强学习，分布式系统，车辆互联网（IOV）和新燃料技术外，在进一步开发GVRP研究方面发挥了重要作用。
Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions reduction, while routing the vehicles. It can be either through adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil fuel vehicles in fleets. GVRP also takes into account environmental sustainability in transportation and logistics. We critically review several variations and specializations of GVRP to address issues related to charging, pickup, delivery, and energy consumption. Starting with the concepts and definitions of GVRP, we summarize the key elements and contributors to GVRP publications. Afterward, the issues regarding each category of green vehicle routing are reviewed, based on which key future research directions and challenges are suggested. It was observed that the main focus of previous publications is on the operational level routing decision and not the supply chain issues. The majority of publications used metaheuristic methods, while overlooking the emerging machine learning methods. We envision that in addition to machine learning, reinforcement learning, distributed systems, the internet of vehicles (IoV), and new fuel technologies have a strong role in developing the GVRP research further.
