Robust Multi-period Fleet Allocation Models for Bike-Sharing Systems
Robust Multi-period Fleet Allocation Models for Bike-Sharing Systems
复制标题
DOI：
10.1007/s11067-013-9203-9
复制
发表时间：
2016-03
影响因子：
2.4
通讯作者：
C. Lu
中科院分区：
文献类型：
--
作者：
C. Lu
给出了共享单车系统各站点间单车日最优分配的数学规划模型。首先，构建了一个时空网络来描述系统中随时间变化的自行车流量。其次，基于时空网络建立了考虑历史平均需求和固定车队规模的自行车车队分配模型。除了在多个时段进行车队分配外，该模型还生成了成本最低的空闲自行车再分配计划，以满足后续时段的需求。该模型旨在纠正共享单车系统中的需求不对称，在这种情况下，从一个站点到另一个站点的流量很少等于相反方向的流量。文中还对该模型进行了扩展，放宽了船队规模限制，以确定最优船队规模，以支持规划阶段的决策。此外，我们使用一些规定的不确定性集来描述不确定的自行车需求，并建立了稳健的自行车车队分配模型，在最坏情况下或由不确定性集得出的最大需求情景下最小化系统总成本。以新北市公共自行车系统为例进行了数值实验，验证了所提模型的适用性和性能。此外，本研究考虑了稳健价格和套期保值两个绩效指标，以考察稳健性和最优性之间的权衡，以及在不确定需求情况下应用稳健解相对于名义最优解的好处。
This paper presents mathematical programming models that generate optimal daily allocation of bicycles to the stations of a bike-sharing system. First, a time-space network is constructed to describe time-dependent bike flows in the system. Next, a bike fleet allocation model that considers average historical demand and fixed fleet size is established based on the time-space network. In addition to fleet allocation in multiple periods, this model generates least cost empty bicycle redistribution plans to meet demand in subsequent time periods. The model aims to correct demand asymmetry in bike-sharing systems, where flow from one station to another is seldom equal to the flow in the opposing direction. An extension of the model that relaxes the fleet size constraint to determine optimal fleet size in supporting planning stage decisions is also presented in the paper. Moreover, we describe uncertain bike demands using some prescribed uncertainty sets and develop robust bike fleet allocation models that minimize total system cost in the worst-case or maximum demand scenarios derived from the uncertainty sets. Numerical experiments were conducted based on the New Taipei City’s public bike system to demonstrate the applicability and performance of the proposed models. In addition, this research considers two performance measures, robust price and hedge value, in order to investigate the tradeoff between robustness and optimality, as well as the benefit of applying robust solutions relative to nominal optimal solutions in uncertain demand situations.