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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| title |
Inventory rebalancing and vehicle routing in bike sharing systems |
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Article |
| creator |
Schuijbroek, J. Hampshire, R.C. van Hoeve, W.-J. |
| subjects |
Bicycles Bike sharing Constraint programming Heuristic Integer programming Inventory Markov processes Ride sharing services Routing Studies |
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European journal of operational research, 2017-03, Vol.257 (3), p.992-1004 |
| description |
•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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eng |
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Elsevier ScienceDirect Journals; Alma/SFX Local Collection |
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ISSN: 0377-2217 |
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0377-2217 1872-6860 |
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European journal of operational research |
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257 |
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3 |
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992 |
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1004 |
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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.</description><identifier>ISSN: 0377-2217</identifier><identifier>EISSN: 1872-6860</identifier><identifier>DOI: 10.1016/j.ejor.2016.08.029</identifier><identifier>CODEN: EJORDT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Bicycles ; Bike sharing ; Constraint programming ; Heuristic ; Integer programming ; Inventory ; Markov processes ; Ride sharing services ; Routing ; Studies</subject><ispartof>European journal of operational research, 2017-03, Vol.257 (3), p.992-1004</ispartof><rights>2016 Elsevier B.V.</rights><rights>Copyright Elsevier Sequoia S.A. Mar 16, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-9ef0799953bec2248e2b0f8d98fc0ba1bd8ada97b220121d3790ef58412dda753</citedby><cites>FETCH-LOGICAL-c451t-9ef0799953bec2248e2b0f8d98fc0ba1bd8ada97b220121d3790ef58412dda753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0377221716306658$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,787,791,3570,27992,27993,46169</link.rule.ids></links><search><creatorcontrib>Schuijbroek, J.</creatorcontrib><creatorcontrib>Hampshire, R.C.</creatorcontrib><creatorcontrib>van Hoeve, W.-J.</creatorcontrib><title>Inventory rebalancing and vehicle routing in bike sharing systems</title><title>European journal of operational research</title><description>•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.</description><subject>Bicycles</subject><subject>Bike sharing</subject><subject>Constraint programming</subject><subject>Heuristic</subject><subject>Integer programming</subject><subject>Inventory</subject><subject>Markov processes</subject><subject>Ride sharing services</subject><subject>Routing</subject><subject>Studies</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKAzEUhoMoWKsv4GrA9YwnmUsScFOKl0LBja5DJjljM7aZmkwLfXsz1LWrc-H_z-Uj5J5CQYE2j32B_RAKlvICRAFMXpAZFZzljWjgksyg5DxnjPJrchNjDwC0pvWMLFb-iH4cwikL2Oqt9sb5r0x7mx1x48wWszAcxqnnfNa6b8ziRoepjqc44i7ekqtObyPe_cU5-Xx5_li-5ev319Vysc5NVdMxl9gBl1LWZYuGsUoga6ETVorOQKtpa4W2WvKWpScYtSWXgF0tKsqs1bwu5-ThPHcfhp8DxlH1wyH4tFJRUbFSCM6bpGJnlQlDjAE7tQ9up8NJUVATKtWrCZWaUCkQKqFKpqezCdP9R4dBRePQG7QuoBmVHdx_9l-AbnJR</recordid><startdate>20170316</startdate><enddate>20170316</enddate><creator>Schuijbroek, J.</creator><creator>Hampshire, R.C.</creator><creator>van Hoeve, W.-J.</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20170316</creationdate><title>Inventory rebalancing and vehicle routing in bike sharing systems</title><author>Schuijbroek, J. ; Hampshire, R.C. ; van Hoeve, W.-J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-9ef0799953bec2248e2b0f8d98fc0ba1bd8ada97b220121d3790ef58412dda753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Bicycles</topic><topic>Bike sharing</topic><topic>Constraint programming</topic><topic>Heuristic</topic><topic>Integer programming</topic><topic>Inventory</topic><topic>Markov processes</topic><topic>Ride sharing services</topic><topic>Routing</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schuijbroek, J.</creatorcontrib><creatorcontrib>Hampshire, R.C.</creatorcontrib><creatorcontrib>van Hoeve, W.-J.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schuijbroek, J.</au><au>Hampshire, R.C.</au><au>van Hoeve, W.-J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inventory rebalancing and vehicle routing in bike sharing systems</atitle><jtitle>European journal of operational research</jtitle><date>2017-03-16</date><risdate>2017</risdate><volume>257</volume><issue>3</issue><spage>992</spage><epage>1004</epage><pages>992-1004</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><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.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2016.08.029</doi><oa>free_for_read</oa></addata></record> |