Behaviorally informed joint optimization of charger placement and dynamic spatio-temporal pricing for electric vehicle networks

The rapid growth of electric vehicles calls for efficient strategies to design charging networks that are both profitable and accessible. This paper develops an integrated framework that jointly optimizes charger placement and dynamic pricing by combining column generation with reinforcement learning. The column generation master problem governs the selection of charger configurations under budget and accessibility constraints, while pricing is modeled as a sequential decision-making problem and solved using reinforcement learning. To address the intractability of the column generation pricing problem, in which reduced costs depend on reinforcement-learning-based pricing outcomes and therefore admit no closed-form expression, we introduce a set of heuristics to generate promising charger configurations. These candidate configurations are subsequently evaluated using reinforcement learning to estimate their expected profitability.