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Most previous research on bike-sharing repositioning problem (BRP) tends to apply backup bikes from depot rather than employ non-repositioning stations, which not only leads to underutilization of resources but also increases repositioning cost. To address this issue, we propose a bike-sharing repositioning model in this paper, and then design an improved hierarchical clustering algorithm which takes into account both the total amount of repositioning and the number of stations within a cluster, thus greatly reducing the problem complexity in large-scale cases. Based on the clustering results, we presented two methods which consider global and local self-balancing intra-cluster repositioning amount (SIRA) to perform repositioning, respectively. Performance of our methods is validated on four instances, and a large number of comparative experiments with Gourbi as well as state-of-the-art algorithms are conducted, which demonstrate that the proposed methods are highly competitive in solving BRPs, especially large BRPs comprising more problem stations.

Solving the bike-sharing repositioning problem considering coordination stations based on self-balancing and clustering strategies