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Abstract |
Public bike sharing systems are important alternatives to motorized individual traffic and are gaining popularity in larger cities worldwide. In order to maintain user satisfaction, operators need to actively re-balance the systems so that each station has enough bikes available for rental as well as sufficient free slots for returning them. This is done by a vehicle fleet that moves bikes among the stations. For this purpose we consider a variable neighborhood search approach that exploits a series of neighborhood structures. While this metaheuristic generates candidate routes for vehicles to visit unbalanced rental stations, the number of bikes to be loaded or unloaded at each stop is efficiently derived by one of three alternative methods based on a greedy heuristic, a maximum flow calculation, and linear programming, respectively. We compare these techniques on instances derived from real-world data and conclude that the simpler approaches benefit from better scalability compared to the linear programming approach. |
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Affiliation |
Bin Hu is assistant professor at the Algorithms and Data Structures Group of Vienna University of Technology. There he received his master degree in 2004 and his PhD degree in 2008. His main research interests lie in the area of combinatorial optimization, especially in applying (meta)heuristics, exact- and hybrid techniques on network design problems, transport optimization and logistics. |
