Dynamic Pricing and Service Fulfillment of Mobile Charging Systems with Stochastic Demands

This paper investigates the operations of a novel service mode, mobile charging, whereby a for-profit platform operator arranges vehicles equipped with charging piles to deliver charging to customers' electric vehicles in low-battery levels. We model the platform-customer interaction with a Stackelberg game. In the concentrated demand scenario, we propose a heuristic service strategy with provable performance guarantees and identify lower and upper bounds for the platform revenues and the price ceiling effect. The analytical results show that the platform revenue's bounds are inversely proportional to the bounds of the individual charging demand distribution and that imposing price ceilings on mobile charging fees can effectively balance the platform’s revenues and customers’ charging costs. In the distributed-demand scenario, customers are scattered across a transportation network and exhibit more complex strategic behaviors. Accordingly, we design an augmented network to transform the complex operations optimization into a variant of vehicle routing problems. To address stochastic charging requests, we develop a customized approximate dynamic programming framework, which achieves a win-win situation for the platform and customers compared to the benchmark. Overall, our work can provide sophisticated guidelines for the operations management of mobile charging services