Bike-sharing systems face persistent spatial imbalances caused by uneven travel demand, making vehicle rebalancing an important operational problem. With the development of shared electric bicycles, traditional rebalancing has evolved into a more complex problem involving both vehicle distribution and battery energy management. This paper reviews the development of rebalancing research from conventional bike-sharing systems to energy-aware e-bike operations. First, static and dynamic rebalancing problems and the main operator-based and user-based strategies are summarized. The additional operational challenges introduced by e-bikes are then discussed, with particular attention to battery states, charging, battery swapping, and integrated rebalancing and energy replenishment. Subsequently, major optimization modeling approaches and solution algorithms are reviewed, including mixed-integer programming, rolling-horizon methods, heuristics, and reinforcement-learning-based approaches. The literature indicates a gradual transition from spatial vehicle redistribution toward integrated spatial-energy coordination and more dynamic and intelligent operational management. Future research should further explore joint rebalancing and energy replenishment, real-time optimization, multi-resource coordination, and the integration of operations research with artificial intelligence.

Yang Lan. From Bike Rebalancing to Energy-Aware E-Bike Operations: A Review of Optimization Models and Solution Algorithms. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 8: 43-49. https://doi.org/10.25236/AJCIS.2026.090806.

[1] Vallez, C.M., Castro, M. and Contreras, D. (2021) Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review. Sustainability, 13, 1829.

[2] Lahoorpoor, B., Faroqi, H., Sadeghi-Niaraki, A. and Choi, S.M. (2019) Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem. Sustainability, 11, 3205.

[3] Jin, X. and Tong, D. (2020) Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges. ISPRS International Journal of Geo-Information, 9, 691.

[4] Xu, M., Di, Y., Zhu, Z., Yang, H. and Chen, X. (2022) Designing Van-Based Mobile Battery Swapping and Rebalancing Services for Dockless Ebike-Sharing Systems Based on the Dueling Double Deep Q-Network. Transportation Research Part C: Emerging Technologies, 138, 103620.

[5] Zhou, Y., Lin, Z., Guan, R. and Sheu, J.B. (2023) Dynamic Battery Swapping and Rebalancing Strategies for E-Bike Sharing Systems. Transportation Research Part B: Methodological, 177, 102820.

[6] Cipriano, M., Colomba, L. and Garza, P. (2021) A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems. Applied Sciences, 11, 6967.

[7] Chiariotti, F., Pielli, C., Zanella, A. and Zorzi, M. (2018) A Dynamic Approach to Rebalancing Bike-Sharing Systems. Sensors, 18, 512.

[8] Ban, S. and Hyun, K.H. (2019) Designing a User Participation-Based Bike Rebalancing Service. Sustainability, 11, 2396.

[9] Wu, R., Liu, S. and Shi, Z. (2019) Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information. Sustainability, 11, 3088.

[10] Zhao, L., Shen, S. and Zhao, Z. (2024) Planning Decentralized Battery-Swapping Recharging Facilities for E-Bike Sharing Systems. Sustainable Cities and Society, 101, 105118.

[11] Emami, E. and Ramezani, M. (2024) Integrated Operator and User-Based Rebalancing and Recharging in Dockless Shared E-Micromobility Systems. Communications in Transportation Research, 4, 100155.

[12] Gao, L., Xu, W. and Duan, Y. (2019) Dynamic Scheduling Based on Predicted Inventory Variation Rate for Public Bicycle System. Sustainability, 11, 1885.

[13] Jia, Y., Zeng, W., Xing, Y., Yang, D. and Li, J. (2021) The Bike-Sharing Rebalancing Problem Considering Multi-Energy Mixed Fleets and Traffic Restrictions. Sustainability, 13, 270.

[14] Pan, B., Tian, L. and Pei, Y. (2023) The Novel Application of Deep Reinforcement to Solve the Rebalancing Problem of Bicycle Sharing Systems with Spatiotemporal Features. Applied Sciences, 13, 9872.

[15] Elhenawy, M., Rakha, H.A., Bichiou, Y., Masoud, M., Glaser, S., Pinnow, J. and Stohy, A. (2021) A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems. Sustainability, 13, 13433.

[16] Lim, H., Chung, K. and Lee, S. (2024) Multi-Objective Optimization of Pick-Up and Delivery Operations in Bike-Sharing Systems Using a Hybrid Genetic Algorithm. Applied Sciences, 14, 6703.

[17] Zou, Z., Yan, X., Liu, P., Yang, W. and Zhang, C. (2025) Sustainable Dynamic Scheduling Optimization of Shared Batteries in Urban Electric Bicycles: An Integer Programming Approach. Sustainability, 17, 4379.

[18] Albuquerque, V., Sales Dias, M. and Bacao, F. (2021) Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review. ISPRS International Journal of Geo-Information, 10, 62.