THESIS
2019
xi, 64 pages : illustrations ; 30 cm
Abstract
Recently, transportation-as-a-service (TaaS) becomes an increasing trend, and online car-hailing
companies start to apply Electric Vehicles (EV) to serve passengers. We make the
market analysis which shows this is a good entrepreneurial opportunity. However, there
are many different challenges compared with traditional vehicle hailing. For instance,
since the charging time of EVs is long and non-negligible, it is necessary to smartly arrange
the charging periods of EVs during the working schedule. Particularly, in order
to maximize the number of accomplished tasks, online EV taxi platforms assign vehicles
whose left electric power is enough to serve the dynamically arriving tasks, and schedule
suitable idle vehicles to the limited charging stations to recharge. In this thesis te...[
Read more ]
Recently, transportation-as-a-service (TaaS) becomes an increasing trend, and online car-hailing
companies start to apply Electric Vehicles (EV) to serve passengers. We make the
market analysis which shows this is a good entrepreneurial opportunity. However, there
are many different challenges compared with traditional vehicle hailing. For instance,
since the charging time of EVs is long and non-negligible, it is necessary to smartly arrange
the charging periods of EVs during the working schedule. Particularly, in order
to maximize the number of accomplished tasks, online EV taxi platforms assign vehicles
whose left electric power is enough to serve the dynamically arriving tasks, and schedule
suitable idle vehicles to the limited charging stations to recharge. In this thesis technical
part, we focus on solving this challenge. We formally define the power-aware electric vehicle
hailing (PAEVH) problem to serve as many tasks as possible under the constraints
of left power and deadline. However, we prove that the PAEVH problem is NP-hard, and
thus intractable. We design a novel strategy to help arrange the schedules of EVs, and propose
two approximate approaches with theoretical guarantees to adaptively determine
the value of two major parameters of the strategy. Extensive experiments on real-world data sets validate the effectiveness and efficiency of our solutions. Finally, we decide our
business strategy.
Post a Comment