Optimization Online


Hybrid Rebalancing with Dynamic Hubbing for Free-floating Bike Sharing Using Multi-objective Simulation Optimization

Vahid Mahmoodian (mahmoodian***at***mail.usf.edu)
Yu Zhang (yuzhang***at***usf.edu )
Hadi Charkhgard (hcharkhgard***at***usf.edu )

Abstract: For rebalancing problem of free-floating bike sharing systems, we propose dynamic hubbing (i.e. dynamically determining geofencing areas) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, dynamic geofenced areas (hubs) are determined to encourage users to return bikes to desired areas towards the end of the day through a user incentive program. And then for remaining imbalanced bikes, an operator-based rebalancing operation will be scheduled to take care of that. The proposed strategy determines the number of hubs, their locations, the start time for initiating the user incentive program, and the amount of incentives by considering two conflicting objectives, i.e. level of service and rebalancing cost (weighted incentive credits and operating cost for rebalancing the remaining imbalanced bikes). We implement the proposed method to the Share-A-Bull free-floating bike sharing system at the University of South Florida. The results show that incentivizing the users to return the bikes to the hub dynamically determining according to the understanding of imbalance of the system can significantly reduce the total rebalancing cost and improve the level of service.

Keywords: Hub-based rebalancing, User incentive program, Free-floating bike sharing, Simulation, Multi-objective optimization

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Other Topics (Optimization of Simulated Systems )


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Entry Submitted: 01/21/2019
Entry Accepted: 01/22/2019
Entry Last Modified: 01/22/2019

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