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Electric vehicles (EVs) have been highly favoured as a future transportationmode in the transportation section in recent years. EVs have many advantagescompared to traditional transportation, especially the environmental aspect.However, despite many EVs' benefits, operating EVs has limitations in theirusage. One of the significant issues is the uncertainty in their driving range.The driving range of EVs is closely related to their energy consumption, whichis highly affected by exogenous and endogenous factors. Since those factors areunpredictable, uncertainty in EVs' energy consumption should be considered forefficient operation. This paper proposes an adaptive robust optimizationframework for the electric vehicle routing problem. The objective is tominimize the worst-case energy consumption while guaranteeing that services aredelivered at the appointed time windows without battery level deficiency. Wepostulate that EVs can be recharged en route, and the charging amount can beadjusted depending on the circumstance. The proposed problem is formulated as atwo-stage adaptive robust problem. A column-and-constraint generation basedheuristic algorithm, which is cooperated with variable neighborhood search andalternating direction algorithm, is proposed to solve the proposed model. Thecomputational results show the economic efficiency and robustness of theproposed model, and that there is a tradeoff between the total required energyand the risk of failing to satisfy all customers' demand.

Adaptive robust electric vehicle routing under energy consumption uncertainty