EURO Journal on Transportation and Logistics· 2013Q1
Static repositioning in a bike-sharing system: models and solution approaches
- 520citations
- Q1SCImago
- 2013year
Short summary
This paper introduces novel mixed integer linear programming models for optimizing bike repositioning in bike-sharing systems, achieving high-quality solutions for systems up to 104 stations and two vehicles.
AI-generated from the title and abstract; the full text is not read.
Key points
- Presents two novel mixed integer linear programming (MILP) formulations for static bike repositioning in bike-sharing systems.
- Introduces a generalized model with a convex objective function and time-dependent considerations, extending existing routing models.
- Strengthens formulations with valid inequalities and dominance rules for improved performance.
- Demonstrates high-quality solution generation for instances up to 104 stations and two vehicles.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. EURO Journal on Transportation and Logistics, 2013 · DOI ↗
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Field: Transportation
TransportationSocial Sciences