Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation

The booming car-sharing industry and gig economy embody the critical need for enhancing utilization of vehicular resources. For this emerging business paradigm, the scheduling optimization considering interdependent scenarios and constraints becomes the key issue. In addition, telecommuting jobs are becoming increasingly popular with the support of highly developed telecommunication technologies. This paper develops a framework for optimal scheduling of an electric vehicle owner, who seeks to share the vehicle in a cost-effective manner while ensure sufficient working hours on a telecommuting job at home. The electric vehicle owner aims at minimizing delivery times of customers and charging cost simultaneously, considering a time-of-use charging pricing mechanism, while satisfying customers demands and working hour requirements. To describe this optimal scheduling problem, this paper firstly introduces three elements: state, action and task. Specifically, three states are involved, including charging state, parking state and transporting state. Further, actions of electric vehicle owner are used as the links for state transitions. Finally, multiple tasks are completed sequentially in a multi-temporal time horizon, where each task is assigned with a selected state. The proposed model formulates the scheduling problem for each state. Moreover, supplementary constraints representing state transitions, working hour requirement and energy neutral position of an electric vehicle battery are further incorporated to establish the optimal scheduling model for the entire process. This paper presents the first-of-its-kind work incorporating all the vital aspects of a well-defined optimal scheduling problem, in which an electric vehicle owner seeks car-sharing opportunity and conducts a telecommuting job at home, considering multiple states, actions and tasks in a continuous multi-temporal horizon. The numerical studies demonstrate the effectiveness of the designed framework in terms of optimizing routing selections and charging time allocation. The performance of the proposed model is also compared with baseline scenarios, and it finds that up to 18.5% cost saving can be accomplished by adopting the proposed model, which validates its cost benefits.