Data-driven optimization for rebalancing shared electric scooters
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 optim...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-09-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024249 |
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| _version_ | 1832590789681610752 |
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| author | Yanxia Guan Xuecheng Tian Sheng Jin Kun Gao Wen Yi Yong Jin Xiaosong Hu Shuaian Wang |
| author_facet | Yanxia Guan Xuecheng Tian Sheng Jin Kun Gao Wen Yi Yong Jin Xiaosong Hu Shuaian Wang |
| author_sort | Yanxia Guan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-014306b0767d4bfa8b14e9bed2d583e6 |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-014306b0767d4bfa8b14e9bed2d583e62025-01-23T07:52:42ZengAIMS PressElectronic Research Archive2688-15942024-09-013295377539110.3934/era.2024249Data-driven optimization for rebalancing shared electric scootersYanxia Guan0Xuecheng Tian1Sheng Jin2Kun Gao3Wen Yi4Yong Jin5Xiaosong Hu6Shuaian Wang7Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong KongFaculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong KongCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaDepartment of Architecture and Civil Engineering, Chalmers University of Technology, Chalmers, SwedenDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong KongFaculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong KongState Key Laboratory of Mechanical Transmission/Automotive Collaborative Innovation Center, Chongqing University, Chongqing, ChinaFaculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong KongShared 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.https://www.aimspress.com/article/doi/10.3934/era.2024249data-driven optimizationrebalancing problemshared electric scootersuncertain user demand |
| spellingShingle | Yanxia Guan Xuecheng Tian Sheng Jin Kun Gao Wen Yi Yong Jin Xiaosong Hu Shuaian Wang Data-driven optimization for rebalancing shared electric scooters Electronic Research Archive data-driven optimization rebalancing problem shared electric scooters uncertain user demand |
| title | Data-driven optimization for rebalancing shared electric scooters |
| title_full | Data-driven optimization for rebalancing shared electric scooters |
| title_fullStr | Data-driven optimization for rebalancing shared electric scooters |
| title_full_unstemmed | Data-driven optimization for rebalancing shared electric scooters |
| title_short | Data-driven optimization for rebalancing shared electric scooters |
| title_sort | data driven optimization for rebalancing shared electric scooters |
| topic | data-driven optimization rebalancing problem shared electric scooters uncertain user demand |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024249 |
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