Vehicle Rebalancing in a Shared Micromobility System with Rider Crowdsourcing

Ziliang Jin (), Yulan Wang (), Yun Fong Lim (), Kai Pan () and Zuo-Jun Max Shen ()
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Ziliang Jin: Faculty of Business, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Yulan Wang: Faculty of Business, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Yun Fong Lim: Lee Kong Chian School of Business, Singapore Management University, Singapore 188065, Singapore
Kai Pan: Faculty of Business, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Zuo-Jun Max Shen: College of Engineering, University of California, Berkeley, Berkeley, California 94720; Faculty of Engineering and Faculty of Business and Economics, The University of Hong Kong, Hong Kong

Manufacturing & Service Operations Management, 2023, vol. 25, issue 4, 1394-1415

Abstract: Problem definition : Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results : We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications : The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL.

Keywords: shared micromobility; crowdsourcing; allocation and relocation; two-stage stochastic mixed-integer programming; decomposition algorithm (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:4:p:1394-1415

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