Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning
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ijcai19.pdf
Description
Accepted version
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347.09 KB
Format
Adobe PDF
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Author(s)
Ghosh, Supriyo
Koh, Jing Yu
Jaillet, Patrick
Date Issued
August 2019
Journal
IJCAI International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence Organization
Citation
Ghosh, Supriyo et al. "Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning." IJCAI International Joint Conference on Artificial Intelligence, August 2019, Macao, International Joint Conferences on Artificial Intelligence Organization, August 2019. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.
Version
Author's final manuscript
Abstract
In bike sharing systems (BSSs), the uncoordinated movements of customers using bikes lead to empty or congested stations, which causes a significant loss in customer demand. In order to reduce the lost demand, a wide variety of existing research has employed a fixed set of historical demand patterns to design efficient bike repositioning solutions. However, the progress remains slow in understanding the underlying uncertainties in demand and designing proactive robust bike repositioning solutions. To bridge this gap, we propose a dynamic bike repositioning approach based on a probabilistic satisficing method which uses the uncertain demand parameters that are learnt from historical data. We develop a novel and computationally efficient mixed integer linear program for maximizing the probability of satisfying the uncertain demand so as to improve the overall customer satisfaction and efficiency of the system. Extensive experimental results from a simulation model built on a real-world bike sharing data set demonstrate that our approach is not only robust to uncertainties in customer demand, but also outperforms the existing state-of-the-art repositioning approaches in terms of reducing the expected lost demand.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.24963/ijcai.2019/813
