Meng Zhao

Interactive users’ preferences and waiting time together have great impact on charging station network design of electric vehicles (EVs), but only waiting time was considered in previous studies. To fill this research gap, this paper addresses a location planning problem for EV charging stations, which considers users’ preferences and waiting time simultaneously. The problem is formulated as a multi-objective bi-level programming model, the upper level model determines locations and capacity options of charging stations with the objectives of minimising total cost and minimising total service tardiness, and the lower level model determines the allocation of users to stations with the objective of minimising total travel time. A hybrid non-dominated sorting genetic algorithm II (HNSGA-II) with embedded level determination algorithm (LDA) and a partial enumeration algorithm (PEA) are proposed, respectively, to solve the model. Furthermore, managerial analysis is implemented to verify the advantages of considering users’ preferences in reducing charging service tardiness and saving cost compared with the mode of no considering users’ preferences. And sensitivity analysis is also performed to provide managerial insights for EV charging station location practice. Finally, a real-world case study is conducted to verify the applicability of the proposed approach in solving practical location planning problems.

App-based transportation service system, such as Uber and Didi, has brought a new transportation mode to users, who are able to make reservations using mobile apps conveniently. However, one of the fundamental challenges in app-based transportation system is the inefficiency and unreliability of the vehicle routing plans caused by complex topology of urban road network and unpredictable traffic conditions. A common way to tackle this problem is repositioning pickup or delivery locations via the coordination between drivers and passengers. This paper studies an on-demand ridesharing problem that determines the optimal ride-share matching strategy and vehicle routing plan with respect to flexible pickup and delivery locations. By introducing the concept of space-time windows, the problem is formulated as the pickup and delivery problem with space-time windows (PDPSW) in space-time network. To solve the model efficiently and accurately, we particularly develop a customized solution approach based on Lagrangian relaxation. Numerical examples are conducted to demonstrate the performance of the proposed framework and draw some managerial insights into the optimal system operation. The results indicate that adopting the serving strategy of flexible pickup and delivery locations will evidently reduce the system cost and improve the service quality in app-based transportation service systems.