Station-based bike-sharing systems provide users with inexpensive one-way bike rides. A major challenge for operators of BSSs lies in redistributing bikes so that users may take the bike rides they request. Existing research on tactical planning proposes optimization models for designing redistribution plans that a vehicle fleet implements on a daily basis. The purpose of this paper is to identify the value and limitations of stochastic programming for bike-sharing redistribution and to understand the efficacy of the obtained plans once they are implemented. To this end, we first analyze the variability in recorded ride data from three North American bike-sharing systems which mainly differ in the intensity of commuting. The results of the data analysis show that stations that are mainly used by commuters display less variability in demand than stations that are mainly used for other ride purposes like errands and leisure. To assess the effect of demand variability on the operational implementation of redistribution plans, we rely on agent-based simulation. In the simulation, vehicle tours are implemented as planned. However, since redistribution plans are designed based on demand forecasts of ride requests, guidance is needed about how to adjust the bike quantities to pick up from or deliver to each station when actual ride requests are observed. Therefore, we propose rule-based procedures to adjust redistribution decisions when the numbers of bikes at stations and vehicle loads differ from the setting considered by optimization. We show that demand variability is a leading indicator about whether redistribution plans perform well operationally.

基于站点的自行车共享系统为用户提供廉价的单程自行车骑行。BSS 运营商面临的一个主要挑战在于重新分配自行车，以便用户可以骑他们要求的自行车。现有的战术规划研究提出了用于设计车队每天实施的再分配计划的优化模型。本文的目的是确定随机编程对共享单车再分配的价值和局限性，并了解所获得的计划一旦实施的效果。为此，我们首先分析了来自三个北美共享单车系统的记录骑行数据的可变性，这些系统主要在通勤强度上有所不同。数据分析的结果表明，与主要用于其他乘车目的（如跑腿和休闲）的车站相比，主要供通勤者使用的车站的需求变化较小。为了评估需求变化对再分配计划运营实施的影响，我们依赖于基于代理的模拟。在模拟中，车辆游览按计划进行。但是，由于重新分配计划是根据乘车请求的需求预测设计的，因此需要指导如何在观察到实际乘车请求时调整从每个站点取车或运送到每个站点的自行车数量。因此，当车站的自行车数量和车辆负载与优化考虑的设置不同时，我们提出了基于规则的程序来调整重新分配决策。