Static repositioning in a bike-sharing system: models and solution approaches

By Tal Raviv, Michal Tzur, Iris A. Forma. EURO Journal on Transportation and Logistics, 2013

553 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/5f443506b9dc9c9fae1f018e0e3d75112308a67b

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Citations in the corpus

Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity (2012, background): “[23] study the optimal placement of bikes at the beginning of a day.”
An optimization framework for the development of efficient one-way car-sharing systems (2015, background): “Additional formulations of the static balancing problem have been proposed in Raviv, Tzur, and Forma (2013).”
Ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in China (2014, background): “, 2010), bikesharing planning strategic (Lin and Yang, 2011), and system rebalancing (Chemla et al., 2012; Raviv et al., 2013; Nair and Miller-Hooks, 2014).”
Bike Sharing and the Economy, the Environment, and Health-Related Externalities (2018, background): “Some quantitative studies have taken a closer look at bike-sharing safety and the repositioning problem [28,29].”
Dynamic Vehicle Redistribution and Online Price Incentives in Shared Mobility Systems (2013, background): “r to attain a predeﬁned ﬁll level for each station, prior to customers interacting with the system (e.g. during the night). For example, [16] present a solution for the routing of a single truck, and [17] consider the case of multiple trucks. The advantage of this approach is that there is ample tim”
Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS) (2018, unknown): “Similar approaches have been developed for bike-sharing [30].”
Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City (2017, methodology): “…problematic stations, efficiency of operator rebalancing program, and proposing new methods for optimizing bicycle rebalancing operations and repositioning trucks’ routing schemes (Vogel and Mattfeld, 2011; Nair et al., 2013; Raviv et al., 2013; Fricker and Gast, 2014; Pfrommer et al., 2014).”
Inventory rebalancing and vehicle routing in bike sharing systems (2017, methodology): “We define the Routing Problem, a pure MIP approach to the Bike Sharing Rebalancing Problem, inspired by Raviv et al. (2011a).”