Enhancing Hybrid Bike-Sharing Systems Through Long-Term Inventory Management: A Generative-Model-Informed Reinforcement Learning Approach

Hybrid bike-sharing systems (operating both bikes and ebikes) are emerging worldwide for flexibility and sustainability. Excessive rebalancing, while addressing supply-demand imbalances, causes financial burdens and resource waste. Hence, long-term daily operations (e.g., rebalancing at midnights for upcoming days) have practical economic significance, but they remain underexplored due to difficulties in tracking high within-day and between-day demand variability and the interplay between demand and daily inventory management strategies. Besides, cooperative operation of bikes and ebikes expands solution space, given their inherent demand coupling. This study is the first to explore long-term daily inventory strategies for the hybrid system (ebikes get charged at stations), determining spatial-heterogeneous inventory at midnight to maximize profits. To address the demand variability perception issue, we develop a recurrent-attentive neural process (RANP) model to predict hour-to-hour demand of the upcoming day. The RANP is integrated into a long-term optimization model, referred to as the generative-models-informed Markov decision process (GMI-MDP), where two cooperative intelligent agents determine the bike–ebike allocation based on demand perception and system rewards. A suite of numerical experiments utilizing a real-world dataset from New York is carried out, and various strategies are compared. The proposed method, through online and offline tests, demonstrates superiority over other MDP-based and rule-based methods, achieving a faster solution-seeking process and more stable rewards than MDP with exact upcoming demand. By comparing inventory volatility, we offer insights into managerial operations.