  Electric vehicles (EVs) have been highly favoured as a future transportation
mode in the transportation section in recent years. EVs have many advantages
compared to traditional transportation, especially the environmental aspect.
However, despite many EVs' benefits, operating EVs has limitations in their
usage. One of the significant issues is the uncertainty in their driving range.
The driving range of EVs is closely related to their energy consumption, which
is highly affected by exogenous and endogenous factors. Since those factors are
unpredictable, uncertainty in EVs' energy consumption should be considered for
efficient operation. This paper proposes an adaptive robust optimization
framework for the electric vehicle routing problem. The objective is to
minimize the worst-case energy consumption while guaranteeing that services are
delivered at the appointed time windows without battery level deficiency. We
postulate that EVs can be recharged en route, and the charging amount can be
adjusted depending on the circumstance. The proposed problem is formulated as a
two-stage adaptive robust problem. A column-and-constraint generation based
heuristic algorithm, which is cooperated with variable neighborhood search and
alternating direction algorithm, is proposed to solve the proposed model. The
computational results show the economic efficiency and robustness of the
proposed model, and that there is a tradeoff between the total required energy
and the risk of failing to satisfy all customers' demand.
