As the popularity and usage of bike-sharing systems increase, a better decision-making model tailored for the successful operations of bike-sharing systems is needed. This study is motivated to address operator-based inventory rebalancing of bike-sharing systems, and the main objective is to develop a mathematical optimization model designed to derive an optimal daily inventory rebalancing plan. Specifically, this study proposes a risk-averse two-stage stochastic programming to determine optimal initial inventory levels for each station to minimize operational costs for relocating bikes and expected penalty costs due to unmet requests. This study adopts the conditional value at risk to properly measure the risk associated with unmet requests to implement risk-averse stochastic programming. Numerical experiments are conducted based on scenario data generated by empirical distributions fitted to trip data from the Houston BCycle to validate and evaluate the proposed model. The results show that the proposed model can be successfully applied to inventory rebalancing to improve the usability of bike-sharing systems.

中文翻译：

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共享单车系统库存再平衡的风险规避两阶段随机规划

随着自行车共享系统的普及和使用的增加，需要为自行车共享系统的成功运营量身定制更好的决策模型。本研究的目的是解决基于运营商的自行车共享系统的库存再平衡问题，主要目标是开发一个数学优化模型，旨在得出最佳的每日库存再平衡计划。具体来说，本研究提出了一种规避风险的两阶段随机规划，以确定每个站点的最佳初始库存水平，以尽量减少重新安置自行车的运营成本以及因未满足请求而导致的预期罚款成本。本研究采用条件风险价值来正确衡量与实施风险规避随机规划的未满足请求相关的风险。基于与休斯顿 BCycle 的行程数据拟合的经验分布生成的场景数据进行数值实验，以验证和评估所提出的模型。结果表明，所提出的模型可以成功应用于库存再平衡，以提高自行车共享系统的可用性。