Data-driven optimization for rebalancing shared electric scooters
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| Titel: | Data-driven optimization for rebalancing shared electric scooters |
|---|---|
| Autoren: | Guan, Yanxia, Tian, Xuecheng, Jin, Sheng, Gao, Kun, 1993, Yi, Wen, Jin, Yong, Hu, Xiaosong, Wang, Shuaian |
| Quelle: | Electronic Research Archive. 32(9):5377-5391 |
| Schlagwörter: | rebalancing problem, data-driven optimization, uncertain user demand, shared electric scooters |
| Beschreibung: | Shared electric scooters have become a popular and flexible transportation mode in recent years. However, managing these systems, especially the rebalancing of scooters, poses significant challenges due to the unpredictable nature of user demand. To tackle this issue, we developed a stochastic optimization model (M0) aimed at minimizing transportation costs and penalties associated with unmet demand. To solve this model, we initially introduced a mean-value optimization model (M1), which uses average historical values for user demand. Subsequently, to capture the variability and uncertainty more accurately, we proposed a data-driven optimization model (M2) that uses the empirical distribution of historical data. Through computational experiments, we assessed both models’ performance. The results consistently showed that M2 outperformed M1, effectively managing stochastic demand across various scenarios. Additionally, sensitivity analyses confirmed the adaptability of M2. Our findings offer practical insights for improving the efficiency of shared electric scooter systems under uncertain demand conditions. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/543304 https://research.chalmers.se/publication/543264 https://research.chalmers.se/publication/543304/file/543304_Fulltext.pdf |
| Datenbank: | SwePub |
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| Items | – Name: Title Label: Title Group: Ti Data: Data-driven optimization for rebalancing shared electric scooters – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guan%2C+Yanxia%22">Guan, Yanxia</searchLink><br /><searchLink fieldCode="AR" term="%22Tian%2C+Xuecheng%22">Tian, Xuecheng</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Sheng%22">Jin, Sheng</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Kun%22">Gao, Kun</searchLink>, 1993<br /><searchLink fieldCode="AR" term="%22Yi%2C+Wen%22">Yi, Wen</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Yong%22">Jin, Yong</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Xiaosong%22">Hu, Xiaosong</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shuaian%22">Wang, Shuaian</searchLink> – Name: TitleSource Label: Source Group: Src Data: <i>Electronic Research Archive</i>. 32(9):5377-5391 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22rebalancing+problem%22">rebalancing problem</searchLink><br /><searchLink fieldCode="DE" term="%22data-driven+optimization%22">data-driven optimization</searchLink><br /><searchLink fieldCode="DE" term="%22uncertain+user+demand%22">uncertain user demand</searchLink><br /><searchLink fieldCode="DE" term="%22shared+electric+scooters%22">shared electric scooters</searchLink> – Name: Abstract Label: Description Group: Ab Data: Shared electric scooters have become a popular and flexible transportation mode in recent years. However, managing these systems, especially the rebalancing of scooters, poses significant challenges due to the unpredictable nature of user demand. To tackle this issue, we developed a stochastic optimization model (M0) aimed at minimizing transportation costs and penalties associated with unmet demand. To solve this model, we initially introduced a mean-value optimization model (M1), which uses average historical values for user demand. Subsequently, to capture the variability and uncertainty more accurately, we proposed a data-driven optimization model (M2) that uses the empirical distribution of historical data. Through computational experiments, we assessed both models’ performance. The results consistently showed that M2 outperformed M1, effectively managing stochastic demand across various scenarios. Additionally, sensitivity analyses confirmed the adaptability of M2. Our findings offer practical insights for improving the efficiency of shared electric scooter systems under uncertain demand conditions. – Name: Format Label: File Description Group: SrcInfo Data: electronic – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543304" linkWindow="_blank">https://research.chalmers.se/publication/543304</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543264" linkWindow="_blank">https://research.chalmers.se/publication/543264</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543304/file/543304_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/543304/file/543304_Fulltext.pdf</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3934/ERA.2024249 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 5377 Subjects: – SubjectFull: rebalancing problem Type: general – SubjectFull: data-driven optimization Type: general – SubjectFull: uncertain user demand Type: general – SubjectFull: shared electric scooters Type: general Titles: – TitleFull: Data-driven optimization for rebalancing shared electric scooters Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guan, Yanxia – PersonEntity: Name: NameFull: Tian, Xuecheng – PersonEntity: Name: NameFull: Jin, Sheng – PersonEntity: Name: NameFull: Gao, Kun – PersonEntity: Name: NameFull: Yi, Wen – PersonEntity: Name: NameFull: Jin, Yong – PersonEntity: Name: NameFull: Hu, Xiaosong – PersonEntity: Name: NameFull: Wang, Shuaian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 26881594 – Type: issn-locals Value: SWEPUB_FREE – Type: issn-locals Value: CTH_SWEPUB Numbering: – Type: volume Value: 32 – Type: issue Value: 9 Titles: – TitleFull: Electronic Research Archive Type: main |
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