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

This study investigates the operations of a novel service model, mobile charging, in which an e-commerce platform operator dispatches trucks equipped with charging piles to provide charging services for customers’ electric vehicles with low battery levels. We model the platform-customer interaction with a Stackelberg game and explicitly characterize customers’ optimal charging decisions under the platform’s various service plans. In a general scenario that involves charging requests within a transportation network, we develop a joint optimization model for the platform’s pricing and service fulfillment, utilizing an elaborately constructed augmented network. In addition, we explore a localized subproblem where multiple orders are concentrated within a specific region. With a simplified model structure, we propose an approximation algorithm with provable performance guarantees and further theoretically evaluate the resource consumption and associated platform benefits for serving the orders in each region. The results enable the batching of neighboring demands within the general scenario as a specialized node, resulting in a streamlined network with fewer nodes. Additionally, we can enhance the algorithm’s efficiency within the general framework through strategic prioritization of node visitations, leveraging the analytical findings. Furthermore, the insights derived can offer recommendations for the deployment of mobile charging and the selection of target areas in the initial stages. Overall, our study provides comprehensive guidelines and valuable insights for mobile charging operations.