Inventory rebalancing and vehicle routing in bike sharing systems
•We derive service level bounds by modeling inventory as a non-stationary Markov chain.•Mixed-integer programming for multi-vehicle rebalancing is practically intractable.•Our polynomial-size clustering heuristic maintains service level feasibility.•We provide computational results on data from Bost...
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| Published in: | European journal of operational research 2017-03, Vol.257 (3), p.992-1004 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •We derive service level bounds by modeling inventory as a non-stationary Markov chain.•Mixed-integer programming for multi-vehicle rebalancing is practically intractable.•Our polynomial-size clustering heuristic maintains service level feasibility.•We provide computational results on data from Boston, MA and Washington, DC.•Our heuristic outperforms mixed-integer and constraint programming approaches.
Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which a polynomial-size Clustering Problem simultaneously considers the service level feasibility and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed-integer programming formulation and a constraint programming approach. |
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| ISSN: | 0377-2217 1872-6860 |