Integrated optimization of planning and operation of a shared automated electric vehicle system considering the trip selection and opportunity cost

Shared autonomous electric vehicle systems (SAEVS) combine autonomous driving technology with shared electric vehicle services to provide advantages over traditional shared vehicle systems, including autonomous vehicle relocation and rapid response to user needs. In this study, we seek to enhance the operational efficiency and profitability of SAEVS by considering trip selection and the potential opportunity cost associated with unmet user demands. An integer linear programming (ILP) model is developed using a spatio-temporal state network to optimize the system design planning (e.g., charging facility, vehicle fleet sizing and distribution) and operational decisions (e.g., vehicle operational relocation and trip selection strategy). To handle the computational complexities of this model, we propose a Lagrangian relaxation (LR) algorithm. The performance of the LR algorithm is evaluated through a case study. The results, along with a parameter sensitivity analysis, reveal several key findings: (i) Allocating vehicles to stations with concentrated early peak demand, distributing charging facilities to stations with high total demand throughout the day and implementing vehicle relocation after the early demand peak can mitigate uneven vehicle distribution; (ii) Implementing trip selection enhances SAEVS profitability; (iii) Increasing opportunity cost meets user demands but at the expense of reduced profit; (iv) It is recommended that SAEVS be equipped with charging facilities of suitable charging power based on operational conditions.