{"type":"ScholarlyArticle","title":"Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning","authors":[{"name":"Supriyo Ghosh","slug":"supriyo-ghosh","given":"Supriyo","family":"Ghosh","affiliation":"IBM Research Center , Singapore 018983"},{"name":"Jing Yu Koh","slug":"jing-yu-koh","given":"Jing Yu","family":"Koh","affiliation":"Singapore University of Technology and Design , Singapore"},{"name":"Patrick Jaillet","slug":"patrick-jaillet","given":"Patrick","family":"Jaillet","affiliation":"Department of Electrical Engineering and Computer Science , Massachusetts Institute of Technology"}],"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\r\ndemand 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.","doi":"10.24963/ijcai.2019/813","url":"https://ijcai.org/proceedings/2019/813","pdf":"https://ijcai.org/proceedings/2019/0813.pdf","markdown":"https://ijcai.org/proceedings/2019/813.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/813","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":813,"pages":{"first":5864,"last":5870},"keywords":["Special Track on AI for Improving Human-Well Being: Computational sustainability (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: AI benefits to society AI applications (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Boyd and Vandenberghe, 2004] Stephen Boyd and Lieven Vandenberghe. 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