Bike-sharing systems have become increasingly popular, providing a convenient, cost-effective, and environmentally friendly transportation option for urban commuters on short trips. However, an efficient and sustainable bike-sharing system faces a key challenge to dynamically balancing the supply and demand of bicycles through efficient inventory routing. This paper introduces a comprehensive combinatorial framework that tackles the critical challenges in the bike-sharing system's inventory routing problem. Firstly, we present a novel mathematical model that considers multiple delivery vehicle types and incorporates important factors like dispatch cost, service time, and user satisfaction, all while ensuring fair scheduling. The comprehensiveness of our model makes it highly applicable to real-world scenarios, addressing practical concerns faced by bike-sharing companies. Secondly, we leverage reinforcement learning mechanisms to gather quantitative information on the spatial and temporal patterns of demand and supply. With this data, we construct an effective regression model that accurately predicts station demand. Additionally, we propose an efficient heuristic approach to generate service sequences for delivery vehicle dispatching. Our approach employs a far-sighted strategy-based local iterative search algorithm to construct solutions, coupled with an adaptive exploration algorithm to continually improve solution quality. The proposed solution method is an innovative integration of reinforcement learning, demand prediction, and heuristic-based dispatching, significantly enhancing solution quality and computational efficiency. By bridging the gap between academic research and real-world practice, our framework offers practical and effective solutions for bike-sharing systems. Finally, we validate our proposed framework with extensive experimental results using real-world datasets. Our approach outperforms state-of-the-art algorithms within a short computational time, demonstrating its superiority in terms of solution quality compared to prior literature. Our research opens a new, viable direction for industrial practice, providing valuable insights for decision-makers to optimize bicycle inventory management in a smarter and more efficient way.

共享单车系统日益普及，为城市短途通勤者提供了便捷、经济、环保的交通选择。然而，高效且可持续的自行车共享系统面临着一个关键挑战，即通过有效的库存路线动态平衡自行车的供需。本文介绍了一个全面的组合框架，该框架可解决自行车共享系统库存路径问题中的关键挑战。首先，我们提出了一种新颖的数学模型，该模型考虑了多种送货车辆类型，并结合了调度成本、服务时间和用户满意度等重要因素，同时确保公平调度。我们模型的全面性使其高度适用于现实场景，解决了共享单车公司面临的实际问题。其次，我们利用强化学习机制来收集有关需求和供给的时空模式的定量信息。利用这些数据，我们构建了一个有效的回归模型，可以准确预测车站需求。此外，我们提出了一种有效的启发式方法来生成用于送货车辆调度的服务序列。我们的方法采用基于远见策略的局部迭代搜索算法来构建解决方案，并结合自适应探索算法来不断提高解决方案的质量。所提出的解决方法是强化学习、需求预测和启发式调度的创新集成，显着提高了解决方案质量和计算效率。通过弥合学术研究与现实世界实践之间的差距，我们的框架为自行车共享系统提供了实用且有效的解决方案。最后，我们使用真实世界的数据集通过广泛的实验结果验证了我们提出的框架。我们的方法在很短的计算时间内超越了最先进的算法，证明了与之前的文献相比，它在解决方案质量方面的优越性。我们的研究为工业实践开辟了一个新的、可行的方向，为决策者以更智能、更高效的方式优化自行车库存管理提供了宝贵的见解。