TY - GEN
T1 - Two-Level Trip Selection and Price Incentive Scheduling in Electric Vehicle-Sharing System
AU - Jiao, Zihao
AU - Liu, Xin
AU - Ran, Lun
AU - Zhang, Yuli
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The rebalance operations have been an essential problem in car-sharing service. In this paper, a two-level price incentive trip selection process is proposed to mitigate the imbalance issue in an electric vehicle-sharing (EVS) system. Specifically, at the perspective of customers, a trip price plan is made based on the adoption rate incorporating stochastic utility function in the first-level trip selection. The second-level selection adopts part of customers kept in the first-level selection, which brings less reposition cost happened in the scheduling operations in the EVS service. In the two-level trip selection process, the uncertain parameters, i.e., customers’ price expectation, potential travel demand, are assumed as random variables with known statistical measures, e.g., marginal moments, obtained from the real world. And the corresponding worst-case chance constraints combined with these random variables are further approximated as the convex optimization. In a real-world case study, the computational results demonstrate several economic and environmental benefits of our two-level selection program in the EVS system.
AB - The rebalance operations have been an essential problem in car-sharing service. In this paper, a two-level price incentive trip selection process is proposed to mitigate the imbalance issue in an electric vehicle-sharing (EVS) system. Specifically, at the perspective of customers, a trip price plan is made based on the adoption rate incorporating stochastic utility function in the first-level trip selection. The second-level selection adopts part of customers kept in the first-level selection, which brings less reposition cost happened in the scheduling operations in the EVS service. In the two-level trip selection process, the uncertain parameters, i.e., customers’ price expectation, potential travel demand, are assumed as random variables with known statistical measures, e.g., marginal moments, obtained from the real world. And the corresponding worst-case chance constraints combined with these random variables are further approximated as the convex optimization. In a real-world case study, the computational results demonstrate several economic and environmental benefits of our two-level selection program in the EVS system.
KW - Car-sharing service
KW - Electric vehicles
KW - Price incentive policy
KW - Repositioning scheduling
KW - Robust scheduling
UR - http://www.scopus.com/inward/record.url?scp=85126134376&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30967-1_18
DO - 10.1007/978-3-030-30967-1_18
M3 - Conference contribution
AN - SCOPUS:85126134376
SN - 9783030309664
T3 - Springer Proceedings in Business and Economics
SP - 195
EP - 207
BT - Smart Service Systems, Operations Management, and Analytics - Proceedings of the 2019 INFORMS International Conference on Service Science
A2 - Yang, Hui
A2 - Qiu, Robin
A2 - Chen, Weiwei
PB - Springer Science and Business Media B.V.
T2 - INFORMS International Conference on Service Science, INFORMS-CSS 2019
Y2 - 27 June 2019 through 29 June 2019
ER -