Managing Resources for Shared Micromobility: Approximate Optimality in Large-Scale Systems

• 成果类型: Article
• 署名作者: Akturk, Deniz; Candogan, Ozan; Gupta, Varun
• 署名单位: Washington University (WUSTL); University of Chicago
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.02023
• 发表日期: 2025
• 页码: 5676-5695
• 关键词: Inventory Management; Sharing economy; Reusable Resources; mean-field analysis; deterministic dynamic programming

摘要

We consider the problem of managing resources in shared micromobility systems (bike sharing and scooter sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing of units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model; each period begins with an initial inventory at each node in the network, and then, customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discretetime discounted Markov decision process (MDP) and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean field-based policy with state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data.