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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| Vydáno v: | European journal of operational research Ročník 257; číslo 3; s. 992 - 1004 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
16.03.2017
Elsevier Sequoia S.A |
| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line přístup: | Získat plný text |
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| Abstract | •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. |
|---|---|
| AbstractList | •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. 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. |
| Author | Schuijbroek, J. Hampshire, R.C. van Hoeve, W.-J. |
| Author_xml | – sequence: 1 givenname: J. surname: Schuijbroek fullname: Schuijbroek, J. email: jasper.schuijbroek@gmail.com organization: School of Industrial Engineering, Eindhoven University of Technology, PO Box 513, Eindhoven 5600 MB, Netherlands – sequence: 2 givenname: R.C. surname: Hampshire fullname: Hampshire, R.C. email: hamp@umich.edu organization: University of Michigan Transportation Research Institute, 2901 Baxter Road, Ann Arbor, MI 48109-2150, USA – sequence: 3 givenname: W.-J. surname: van Hoeve fullname: van Hoeve, W.-J. email: vanhoeve@andrew.cmu.edu organization: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA |
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| Keywords | Integer programming Markov processes Routing Constraint programming Inventory Bike sharing |
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| Snippet | •We derive service level bounds by modeling inventory as a non-stationary Markov chain.•Mixed-integer programming for multi-vehicle rebalancing is practically... 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... |
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| SubjectTerms | Bicycles Bike sharing Constraint programming Heuristic Integer programming Inventory Markov processes Ride sharing services Routing Studies |
| Title | Inventory rebalancing and vehicle routing in bike sharing systems |
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