Static repositioning in a bike-sharing system: models and solution approaches
Static repositioning in a bike-sharing system: models and solution approaches
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DOI：
10.1007/s13676-012-0017-6
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发表时间：
2013-08-01
影响因子：
2.4
通讯作者：
Forma, Iris A.
中科院分区：
文献类型：
--
作者：
Raviv, Tal;Tzur, Michal;Forma, Iris A.
自行车共享系统允许人们在分散在城市中的许多自动租赁站中的一个租用自行车，使用它们进行短途旅行，然后在城市的任何一个站点还车。共享单车系统成功的一个关键因素是它有能力满足每个站点对自行车和空置储物柜的不断变化的需求。这是通过重新定位作业的方式实现的，即使用一支专用卡车车队将自行车从一些车站移走，并将其转移到其他车站。在大型自行车共享系统中运营这样一支车队是一个复杂的问题，包括决定车辆应该遵循的路线，以及每次车辆访问时应该在每个车站移走或放置的自行车数量。在这篇文章中，我们提出了我们的建模方法来解决这个问题，推广了现有的文献中的路由模型。这是通过引入唯一的凸目标函数以及与时间相关的考虑来实现的。我们给出了两个混合整数线性规划公式，讨论了与之相关的假设，并通过几个有效的不等式和占优规则加强了它们，并通过大量的数值研究比较了它们的性能。结果表明，其中一个公式对于由104个站点和两辆车组成的实际问题的高质量解是非常有效的。最后，我们总结了好的解决方案的特点。
Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bike-sharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning operation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their performances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions.