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The rising demand for electric vehicles (EVs) worldwide necessitates thedevelopment of robust and accessible charging infrastructure, particularly indeveloping countries where electricity disruptions pose a significantchallenge. Earlier charging infrastructure optimization studies do notrigorously address such service disruption characteristics, resulting insuboptimal infrastructure designs. To address this issue, we propose anefficient simulation-based optimization model that estimates candidatestations' service reliability and incorporates it into the objective functionand constraints. We employ the control variates (CV) variance reductiontechnique to enhance simulation efficiency. Our model provides a highly robustsolution that buffers against uncertain electricity disruptions, even whencandidate station service reliability is subject to underestimation oroverestimation. Using a dataset from Surabaya, Indonesia, our numericalexperiment demonstrates that the proposed model achieves a 13% higher averageobjective value compared to the non-robust solution. Furthermore, the CVtechnique successfully reduces the simulation sample size up to 10 timescompared to Monte Carlo, allowing the model to solve efficiently using astandard MIP solver. Our study provides a robust and efficient solution fordesigning EV charging infrastructure that can thrive even in developingcountries with uncertain electricity disruptions.

A Robust and Efficient Optimization Model for Electric Vehicle Charging Stations in Developing Countries under Electricity Uncertainty