  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.
