Due to the harmful effects of greenhouse gas emissions emitted by petroleum-based vehicles, the transition to alternative fuel vehicles, running on cleaner sources of energy such as electricity, is an important trend. At the beginning of the transition period to alternative-fuel vehicles, due to the lack of comprehensive recharging infrastructure, it is essential to optimally locate recharging stations so that the network is covered as much as possible. Most existing studies on this topic generally assume that the driving range of vehicles and the flow volume are known, however, in practice, they are highly stochastic. The first aim of this paper is to incorporate this type of uncertainty into the problem and formulate it as a robust scenario-based model in which the expected number of drivers who can complete their trip without running out of charge is maximized. The development of a solution algorithm, capable to solve large instances of the model, is another aim followed in this paper. Computational results over different instances taken from the literature or generated randomly confirm the efficiency of the proposed model and algorithms
