System resources allocation optimization through dynamic scheduling is key to improving the service level of bike-sharing. This study innovatively introduces three types of invalid demand with negative effect including waiting, transfer, and abandoning, which consists of the total demand of bike-sharing system. Through exploring the dynamic relationship among users’ travel demands, the quantity and capacity of bikes at the rental points, the records of bicycles borrowed and returned, and the vehicle scheduling schemes, a demand forecasting model for bike-sharing is established. According to the predicted bikes and the maximum capacity limit at each rental point, an optimization model of scheduling scheme is proposed to reduce the invalid demand and the total scheduling time. A two-layer dynamic coupling model with iterative feedback is obtained by combining the demand prediction model and scheduling optimization model and is then solved by Nicked Pareto Genetic Algorithm (NPGA). The proposed model is applied to a case study and the optimal solution set and corresponding Pareto front are obtained. The invalid demand is greatly reduced from 1094 to 26 by an effective scheduling of 3 rounds and 96 minutes. Empirical results show that the proposed model is able to optimize the resource allocation of bike-sharing, significantly reduce the invalid demand caused by the absence of bikes at the rental point such as waiting in a place, walking to other rental points, and giving up for other travel modes, and effectively improve the system service level.

中文翻译：

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考虑无效需求的共享单车动态调度模型

通过动态调度优化系统资源分配是提高自行车共享服务水平的关键。本研究创新地介绍了三种无效需求，它们具有负面影响，包括等待，转移和放弃，这些无效需求包括自行车共享系统的总需求。通过探索用户出行需求，租赁点自行车的数量和容量，借用和归还自行车的记录以及车辆调度方案之间的动态关系，建立了共享自行车的需求预测模型。根据预测的自行车和每个租赁点的最大容量限制，提出一种调度方案的优化模型，以减少无效需求和总调度时间。通过将需求预测模型和调度优化模型相结合，获得了具有迭代反馈的两层动态耦合模型，然后通过尼克·帕累托遗传算法（NPGA）对其进行求解。将该模型应用于案例研究，得到最优解集和相应的帕累托前沿。通过有效安排3轮96分钟，无效需求从1094大大减少到26。实证结果表明，所提出的模型能够优化共享单车的资源分配，显着减少由于在租赁点缺少自行车而导致的无效需求，例如在某个地方等待，步行到其他租赁点以及放弃针对其他出行方式，有效提高系统服务水平。