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Contextual stochastic optimization for determining electric vehicle charging station locations with decision-dependent demand learning
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DOI：10.1016/j.trb.2026.103524.png)
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Abstract
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• Consider a contextual EV charging station location problem under decision-dependent demand. • Propose empirical residuals-based decision-dependent sample average approximation (ER-DD-SAA). • Prove the consistency and asymptotic optimality of ER-DD-SAA. • Introduce a nonlinear regression model to estimate customer demand via two-step regression. • Conduct synthetic experiments and a case study based on real-world data.
Keywords:
Contextual stochastic optimization
Decision-dependent uncertainty
Machine learning
Electric vehicle charging station location problem
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