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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Veröffentlicht in:European journal of operational research Jg. 257; H. 3; S. 992 - 1004
Hauptverfasser: Schuijbroek, J., Hampshire, R.C., van Hoeve, W.-J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 16.03.2017
Elsevier Sequoia S.A
Schlagworte:
Bicycles
Bike sharing
Constraint programming
Heuristic
Integer programming
Inventory
Markov processes
Ride sharing services
Routing
Studies
United States > US
Integer programming
Markov processes
Routing
Constraint programming
Inventory
Bike sharing
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•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.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2016.08.029